Patent Publication Number: US-2023154003-A1

Title: Cell evaluation method, cell evaluation system and program

Description:
TECHNICAL FIELD 
     The present invention relates to a cell evaluation method, a cell evaluation system, and a program. 
     Priority is claimed on Japanese Patent Application No. 2019-198409, filed Oct. 31, 2019, the content of which is incorporated herein by reference. 
     BACKGROUND ART 
     In recent years, a differentiation-inducing method for differentiation of stem cells into differentiated cells has been established and technology for a stable cell culture process has become known. Patent Literature 1 describes technology related to a cell culture process using a method of inducing terminal differentiation of human functional cells based on tissue reconstruction (see Patent Literature 1). Here, the cell culture state is required to be evaluated using an index according to a cell type and each differentiation-inducing process. 
     CITATION LIST 
     Patent Literature 
     Patent Literature 1 
     PCT International Publication No. WO 2013/047639 
     SUMMARY OF INVENTION 
     According to a first aspect of the present invention, there is provided a cell evaluation method including: acquiring a first evaluation index and a first index calculated using the first evaluation index with respect to comparative target cells in a culture process including a cell differentiation-inducing process; calculating a second index on the basis of the first evaluation index with respect to evaluation target cells different from the comparative target cells; and evaluating differentiation of the evaluation target cells by comparing the first index with the second index. 
     According to a second aspect of the present invention, there is provided a cell evaluation system for evaluating cell differentiation in a culture process including a cell differentiation-inducing process, the cell evaluation system including: an acquisition unit configured to acquire a first evaluation index and a first index calculated using the first evaluation index with respect to comparative target cells; a calculation unit configured to calculate a second index on the basis of the first evaluation index with respect to evaluation target cells different from the comparative target cells; and an evaluation unit configured to evaluate differentiation of the evaluation target cells by comparing the first index with the second index. 
     According to a third aspect of the present invention, there is provided a program for evaluating cell differentiation in a culture process including a cell differentiation-inducing process, the program causing a computer to execute: an acquisition step of acquiring a first evaluation index and a first index calculated using the first evaluation index with respect to comparative target cells; a calculation step of calculating a second index on the basis of the first evaluation index with respect to evaluation target cells different from the comparative target cells; and an evaluation step of evaluating differentiation of the evaluation target cells by comparing the first index with the second index. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a schematic diagram showing an example of a culture process until differentiation of induced pluripotent stem (iPS) cells into mature hepatocytes is performed. 
         FIG.  2    is a front view of a culture observation device of the present embodiment. 
         FIG.  3    is a plan view of the culture observation device of the present embodiment. 
         FIG.  4    is a diagram showing an example of a configuration of an image determination device according to a first embodiment. 
         FIG.  5    is a schematic diagram showing an example of a first differentiation-inducing process image. 
         FIG.  6    is a schematic diagram showing an example of a second differentiation-inducing process image. 
         FIG.  7    is a schematic diagram showing an example of a third differentiation-inducing process image. 
         FIG.  8    is a graph showing an example of a determination process of a determination unit according to the first embodiment. 
         FIG.  9    is a flowchart (Part  1 ) showing an example of an operation of the image determination device according to the first embodiment. 
         FIG.  10    is a flowchart (Part  2 ) showing an example of the operation of the image determination device according to the first embodiment. 
         FIG.  11    is a flowchart (Part  3 ) showing an example of the operation of the image determination device according to the first embodiment. 
         FIG.  12    is a graph showing an example of a determination process of a determination unit according to a second embodiment. 
         FIG.  13    is a graph showing an example of a determination process of a determination unit according to Modified Example 1. 
         FIG.  14    is a schematic diagram showing an example of a configuration of an image determination device according to Modified Example 2. 
         FIG.  15    is a schematic diagram showing an example of a culture process until differentiation of iPS cells into mature hepatocytes is performed according to a third embodiment. 
         FIG.  16    is a diagram showing an example of a configuration of the image determination device of the third embodiment. 
         FIG.  17    is a diagram showing an example of cells having a paving stone-shaped form. 
         FIG.  18    is a flowchart showing an example of a process of the image determination device of the third embodiment. 
         FIG.  19    is a diagram showing an overview of light and shade of each part shown in an image P. 
         FIG.  20    is a diagram showing an example of a cell adhesion region. 
         FIG.  21    is a diagram showing an example of a paving stone region. 
         FIG.  22    is a diagram showing an example of a region extracted in step S 744 . 
         FIG.  23    is a diagram conceptually showing a local change. 
         FIG.  24    is a diagram showing an example of a non-cell region. 
         FIG.  25    is a diagram showing an example of an adhesion region. 
         FIG.  26    is a diagram showing an example of a dead cell region. 
         FIG.  27    is a diagram showing an example of a dying and dead cell region. 
         FIG.  28    is a diagram showing an example of an effect quantity of an average value of luminance values in the paving stone region. 
         FIG.  29    is a diagram showing an example of an effect quantity of an area of the cell adhesion region. 
         FIG.  30    is a diagram showing an example of an effect quantity of an area of the adhesion region. 
         FIG.  31    is a diagram showing an example of an effect quantity of an area of the dead cell region. 
         FIG.  32    is a diagram showing an example of an effect quantity of an area of the non-cell region. 
         FIG.  33    is a diagram showing an example of an effect quantity of an area of the dying and dead cell region. 
         FIG.  34    is a diagram showing an example of an operation of the image determination device of the third embodiment. 
         FIG.  35    is a diagram showing an example of an original image. 
         FIG.  36    is a diagram showing an example of a sparse mask. 
         FIG.  37    is a diagram showing an example of a dense mask. 
         FIG.  38    is a diagram showing an example of a muscle mask. 
         FIG.  39    is a diagram showing an example of an operation of an image determination device of a modified example of the third embodiment  FIG.  40    is a diagram showing an example of a relationship between the muscle region and an amount of albumin. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Regarding Mature Hepatocyte Culture Process 
     First, the differentiation of iPS cells into mature hepatocytes, which are an example of mature cells, will be described with reference to  FIG.  1   .  FIG.  1    is a schematic diagram showing an example of a culture process from iPS cells to mature hepatocytes. As shown in  FIG.  1   , the iPS cells differentiate into the mature hepatocytes through a plurality of differentiation-inducing processes. Specifically, iPS cells differentiate into mature hepatocytes (M. H) via endoderm cells (Def. End) and hepatic endoderm cells (Hep. End). In the following description, a process of a period (for example, days 1 to 7) during which iPS cells differentiate into endoderm cells is referred to as a “first differentiation-inducing process,” a process of a period (for example, days 8 to 10) during which the endoderm cells differentiate into hepatic endoderm cells is referred to as a “second differentiation-inducing process,” a process of a period (for example, days 10 to 14) during which hepatic endoderm cells differentiate into mature hepatocytes is referred to as a “third differentiation-inducing process,” and a process of a period (for example, days 15 to 21) during which mature hepatocytes mature appropriately is described as a “fourth differentiation-inducing process.” Although cells to be cultured will be described as iPS cells as an example, the cells to be cultured are not limited to iPS cells and any stem cells having differentiation potency may be used. In addition to iPS cells, stem cells may be existing pluripotent stem cells such as embryonic stem (ES) cells and Muse cells, may be somatic stem cells (tissue stem cells or adult stem cells) capable of differentiating into a plurality of prescribed cell types, or may be unipotent stem cells that differentiate into a specific type of cells. 
     It is determined whether or not the above-described cells have differentiated in each differentiation-inducing process using an index according to each differentiation-inducing process. Here, for example, determining whether or not the cells have differentiated is also evaluating a differentiation progress state, so that it can be paraphrased as evaluating the differentiation. Also, for example, determining whether or not cells have differentiated is determining a degree of maturation of cells in each differentiation-inducing process, so that it can be rephrased as determining whether or not the cells have matured and determining the degree of maturity. For example, when it is determined that the present time is an appropriate time on the basis of index 1 with respect to the iPS cells that have been expanded and cultured, activin is added to a culture medium thereof. Thereby, the iPS cells are induced to differentiate into endoderm cells. Also, it is determined whether or not the endoderm cells have differentiated on the basis of index 2 according to the differentiation-inducing process for the endoderm cells. When it is determined that the cells have differentiated into endoderm cells, bone morphogenetic protein-4 (BMP-4) and fibroblast growth factor-2 (FGF-2) are added to the endoderm cells. Thereby, the endoderm cells are induced to differentiate into hepatic endoderm cells. Also, it is determined whether or not the hepatic endoderm cells have differentiated on the basis of index 3 according to the differentiation-inducing process for the hepatic endoderm cells. Also, it is determined whether or not the mature hepatocytes have differentiated on the basis of index 4 according to the differentiation-inducing process for the mature hepatocytes. Examples of these indices include a difference in color between a region where cells adhere to a culture vessel and a region where cells do not adhere to the culture vessel, a contrast between a cell membrane and cytoplasm, and the like. Also, the mature hepatocytes are, for example, hepatocytes in which an amount of albumin that has been produced is greater than or equal to a known standard. 
     In the conventional method, cells may be visually observed on the basis of these indices to determine whether or not the cells have differentiated. In this case, it may be difficult to perform an appropriate determination process depending on a proficiency level of a person making the determination. A case where the image determination device  10  of the present embodiment uses captured images captured in each differentiation process, determines whether the cells have differentiated on the basis of the index according to each captured image, and performs cell evaluation regardless of the proficiency level of the person making the determination will be described. 
     In the following description, a case where target cells that are evaluated (determined) by the image determination device  10  are hepatocytes will be described as an example. Also, the target cells that are evaluated (determined) by the image determination device  10  may be cells other than hepatocytes. In this case, the number of cell differentiation processes may be less than four or more than four. The target cells that are evaluated (determined) by the image determination device  10  may be, for example, existing cells such as somatic cells (nerve cells, blood cells, pancreatic cells, kidney cells, heart cells, and the like), germ cells, cancer cells, and the like. 
     First Embodiment 
     Hereinafter, a first embodiment of the present invention will be described with reference to the drawings. First, an overview of a culture observation device  11  in the present embodiment will be described with reference to  FIGS.  2  and  3   .  FIG.  2    is a front view of the culture observation device  11  of the present embodiment. Also,  FIG.  3    is a plan view of the culture observation device  11  of the present embodiment. Here, a +X-direction, a −X-direction, a +Y-direction, a −Y-direction, a +Z-direction, and a −Z-direction shown in  FIGS.  2  and  3    are defined. As shown in  FIG.  2   , the +X-direction and the −X-direction are directions along a lateral direction of the culture observation device  11 . The X-direction is opposite to the +X-direction. In the following description, the +X-direction is referred to as a “right” direction, the −X-direction is referred to as a “left” direction, and the +X-direction and the −X-direction are referred to as an “X-direction when they are not distinguished from each other. The +Y-direction and the −Y-direction are directions different from the X-direction (for example, substantially orthogonal thereto). The +Y-direction is a direction that becomes a normal line of an installation surface of the culture observation device  11 . The −Y-direction is opposite to the +Y-direction. In the following description, the +Y-direction is referred to as an “upward” direction, the −Y-direction is referred to as a “downward” direction, and the +Y-direction and the −Y-direction are referred to as a “Y-direction” when they are not distinguished from each other. The +Z-direction and the −Z-direction are directions different from (for example, substantially orthogonal to) the X-direction and the Y-direction. The +Z-direction is a direction along a depth direction of the culture observation device  11 . The −Z-direction is opposite to the +Z-direction. In the following description, the +Z-direction is referred to as a “front” or “forward” direction, the −Z-direction is referred to as a “back” or “backward” direction, and the +Z-direction and the −Z-direction are referred to as a “Z-direction” when they are not distinguished from each other. 
     As shown in  FIGS.  2  and  3   , the culture observation device  11  includes an upper casing  12  and a lower casing  13 . The upper casing  12  and the lower casing  13  are arranged to be in contact with each other vertically via a base plate  14 . The upper casing  12  internally includes a constant temperature compartment  15 , a stocker  21 , an observation unit  22 , a vessel conveyance device  23 , a conveyance table  24 , and an image determination device  10 . 
     There is an opening on a front surface of the constant temperature compartment  15  and this opening is covered with a large door  16 , a middle door  17 , and a small door  18 . Specifically, the large door  16  covers the front surfaces of the upper casing  12  and the lower casing  13  and the middle door  17  covers the front surface of the upper casing  12 . That is, the upper casing  12  has a double door of the large door  16  and the middle door  17 . Thereby, the constant temperature compartment  15  is isolated from an external environment by the middle door  17  even if the large door  16  is opened. The small door  18  is a door for carrying in and out a culture vessel  19  for culturing cells and is attached to the middle door  17 . Thereby, the culture vessel  19  can be carried in and out of the constant temperature room  15  via the small door  18 . Also, it is possible to limit an environmental change of the constant temperature compartment  15  in the case where the small door  18  is opened as compared with the case where the middle door  17  is opened. Airtightness of the large door  16 , the middle door  17 , and the small door  18  is maintained by packings CN1, CN2, and CN3, respectively. 
     Also, a stocker  21 , an observation unit  22 , a vessel conveyance device  23 , and a conveyance table  24  are disposed in the constant temperature compartment  15 . Here, the conveyance table  24  is disposed in front of the small door  18  and the culture vessel  19  is carried in and out from the small door  18 . 
     The stocker  21  is disposed on the left side within the constant temperature compartment  15 . The stocker  21  has a plurality of shelves and a plurality of culture vessels  19  are stored in each shelf of the stocker  21 . Also, in each culture vessel  19 , culture target cells are accommodated together with a culture medium. Also, the culture vessel  19  is, for example, a well plate. The culture vessel  19  is not limited to the well plate and may be an existing culture vessel such as a flask or a dish. 
     The observation unit  22  is disposed so that it is fitted into the opening of the base plate  14 . The observation unit  22  includes a sample table  31 , a stand arm  32  protruding above the sample table  31 , and a main body portion  33  in which a microscopic optical system (not shown) for phase difference observation and an imaging device  34  are embedded. Within the observation unit  22 , the sample table  31  and the stand arm  32  are disposed in the constant temperature compartment  15  and the main body portion  33  is accommodated within the lower casing  13 . The observation unit  22  performs time-lapse observation of the cells within the culture vessel  19 . Details of the time-lapse observation will be described below. 
     The sample table  31  is made of a translucent material and the culture vessel  19  can be placed on the sample table  31 . The sample table  31  is configured to be movable in the horizontal direction and the position of the culture vessel  19  placed on the upper surface can be adjusted. Also, an LED light source (not shown) and an illumination ring diaphragm (not shown) are provided on the stand arm  32  and a light intensity distribution of illumination light from the LED light source with which the sample table  31  is irradiated can be variably adjusted. Also, a condenser lens, an objective lens, a phase plate, and the like are provided on the microscopic optical system (not shown) and the microscopic optical system (not shown) is configured as in an optical system of a known phase contrast microscope. That is, the observation unit  22  functions as a phase contrast microscope. The imaging device  34  acquires a microscopic image of cells by imaging the cells of the culture vessel  19  transmitted and illuminated from above the sample table  31  by the stand arm  32  via the optical system of the microscope. Also, this microscopic image is, for example, a phase difference image in which a phase shift of light is detected as a contrast. 
     The vessel conveyance device  23  has a vertical robot  38  having an articulated arm, a rotary stage  35 , a mini-stage  36 , and an arm portion  37 . The rotary stage  35  is attached to the tip of the vertical robot  38  so that it is rotatable 180° in the horizontal direction via the rotary shaft  35   a . Thus, in the rotary stage  35 , the arm portion  37  can be made to face the stocker  21 , the sample table  31 , and the conveyance table  24 , respectively. The mini-stage  36  is slidably attached to the rotary stage  35  in the horizontal direction. The arm portion  37  for gripping the culture vessel  19  is attached to the mini-stage  36 . Thereby, the vessel conveyance device  23  conveys the culture vessel  19  between the stocker  21 , the sample table  31 , and the conveyance table  24 . 
     Regarding Image Determination Device  10   
       FIG.  4    is a diagram showing an example of a configuration of the image determination device  10  according to the first embodiment. The image determination device  10  implements the imaging device control unit  20  and the control unit  100  as functional units when a hardware processor such as a central processing unit (CPU) executes a program (software) stored in the storage unit. Some or all of the components of the image determination device  10  may be implemented by hardware such as a large-scale integration (LSI) circuit, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a graphics processing unit (GPU) or may be implemented by software and hardware in cooperation. The storage unit  300  is implemented by, for example, a read-only memory (ROM), a flash memory, a secure digital (SD) card, a random access memory (RAM), a register, or the like. 
     The imaging device control unit  20  controls, for example, the vessel conveyance device  23  at prescribed time intervals, places the culture vessel  19  within the stocker  21  on the sample table  31 , and causes the cells within the culture vessel  19  to be imaged by the imaging device  34 . Thereby, the cells within the culture vessel  19  are time-lapse observed. The control unit  100  executes a process of determining whether or not the cells have differentiated on the basis of a captured image obtained by capturing the cells within the culture vessel  19  by the imaging device  34 . 
     Also, the imaging device control unit  20  may be provided separately instead of the configuration provided in the image determination device  10 . In this case, the imaging device control unit  20  and the image determination device  10  are connected so that information can be transmitted and received and the imaging device control unit  20  supplies the image of the cells captured by the imaging device  34  to the image determination device  10 . Also, the image determination device  10  may have a configuration in which it is determined whether or not cells shown in an image have differentiated on the basis of the image of the cells acquired in another method instead of a configuration in which it is determined whether or not the cells within the culture vessel  19  have differentiated on the basis of the image of the cells captured by the imaging device  34  provided in the culture observation device  11 . In this case, the image determination device  10  may not be provided within the culture observation device  11 . 
     The imaging device  34  images the cells being cultured in the culture medium with the elapse of time. Specifically, the imaging device  34  captures an image P, which is an image of cells containing the culture medium, with the elapse of time. The imaging device  34  supplies the captured image P to the image determination device  10 . For convenience of description, the image is an image itself output by the output unit  230  (the display device) to be described below and also represents data (image data) of the image. Here, the image data is, for example, data based on a signal intensity of each pixel of the imaging device  34 . 
     First Differentiation-Inducing Process 
     Hereinafter, an example of the image P captured in the first differentiation-inducing process will be described with reference to  FIG.  5   .  FIG.  5    is a schematic diagram showing an example of a first differentiation-inducing process image P1. The first differentiation-inducing process image P1 is an image in which a culture medium containing cells in the first differentiation-inducing process is imaged among a plurality of images P. As shown in  FIG.  5   , in the first differentiation-inducing process, there are a location where cells adhere and a location where cells do not adhere. Here, the term “cells adhere” indicates that cells adhere to a culture vessel. The image determination device  10  determines whether or not the cells have differentiated on the basis of a non-adhesion ratio UBR in the first differentiation-inducing process. Here, the non-adhesion ratio UBR is a ratio of an area where the cells do not adhere to an area of the culture medium in which the cells are cultured. In the first differentiation-inducing process, the non-adhesion ratio UBR increases as the time elapsed after the cells were cultured decreases and the non-adhesion ratio UBR decreases as the time elapsed after the cells were cultured increases. That is, the non-adhesion ratio UBR decreases as the cell differentiation progresses. 
     Also, the culture vessel is provided with, for example, a layer (for example, a substrate (matrix), an extracellular matrix, an intercellular matrix, or the like) that serves as a scaffold for cells on the surface (the bottom surface) of the culture vessel so that cells can easily adhere thereto. Therefore, the term “cells adhere” also includes a case where cells adhere to the culture vessel via the substrate. In the following description, a case where “cells adhere to the culture vessel” and a case where “cells adhere to the culture vessel via the substrate” are collectively referred to as the case where “cells adhere.” 
     Second Differentiation-Inducing Process 
     Next, an example of an image P captured in the second differentiation-inducing process will be described with reference to  FIG.  6   .  FIG.  6    is a schematic diagram showing an example of the second differentiation-inducing process image P2. The second differentiation-inducing process image P2 is an image in which a culture medium containing cells in the second differentiation-inducing process is imaged among a plurality of images P. As shown in  FIG.  6   , in the second differentiation-inducing process, there are a plurality of cell clusters CC in which cells are clustered. Here, the cell cluster CC is, for example, a colony. The image determination device  10  determines whether or not the cells have differentiated on the basis of an average area CAA in the second differentiation-inducing process. Here, the average area CAA is an average of areas of the cell clusters CC present in the culture medium. In the second differentiation-inducing process, the average area CAA increases as the time elapsed after the cells were cultured decreases and the average area CAA decreases as the time elapsed after the cells were cultured increases. 
     Third Differentiation-Inducing Process 
     Next, an example of an image P captured in a third differentiation-inducing process will be described with reference to  FIG.  7   .  FIG.  7    is a schematic diagram showing an example of a third differentiation-inducing process image P3. The third differentiation-inducing process image P3 is an image in which a culture medium containing cells in the third differentiation-inducing process is imaged among a plurality of images P. As shown in  FIG.  7   , in the third differentiation-inducing process, there are a plurality of cells having an area smaller than the area of the cell cluster CC. The image determination device  10  determines whether or not the cells have differentiated on the basis of a density CPT in the third differentiation-inducing process. Here, the density CPT is a ratio of a perimeter of a certain cell (a cell perimeter L shown in  FIG.  7   ) to a cell area of the certain cell (a cell area CS shown in  FIG.  7   ). The density CPT becomes higher when the cells become more elliptical and becomes lower when the cells become closer to a circle. Also, in the third differentiation-inducing process, the cells become circular as they mature. That is, the density CPT increases as the time elapsed after the cells were cultured decreases and the density CPT decreases as the time elapsed after the cells were cultured increases. 
     In this example, cells differentiate through the differentiation-inducing processes in the order of the first differentiation-inducing process, the second differentiation-inducing process, and the third differentiation-inducing process. That is, the imaging device  34  captures the images P in the order of the first differentiation-inducing process image P1, the second differentiation-inducing process image P2, and the third differentiation-inducing process image P3. Also, the imaging device  34  supplies the images P captured in the order of the first differentiation-inducing process image P1, the second differentiation-inducing process image P2, and the third differentiation-inducing process image P3 to the image determination device  10 . 
     Regarding Differentiation Determination Threshold Value MTH and Determination Transition Threshold Value JTH 
     Returning to  FIG.  4   , the image determination device  10  includes a control unit  100  and a storage unit  300 . The storage unit  300  stores a determination transition threshold value JTH1, a determination transition threshold value JTH2, a differentiation determination threshold value MTH1, a differentiation determination threshold value MTH2, and a differentiation determination threshold value MTH3. The differentiation determination threshold value MTH is an index according to each differentiation-inducing process and is a threshold value indicating a degree of differentiation when it is determined whether or not cells have differentiated. Therefore, the differentiation determination threshold value MTH1 is an index used when it is determined whether or not cells have differentiated in the first differentiation-inducing process, and is a value indicating a prescribed non-adhesion ratio UBR. The differentiation determination threshold value MTH2 is an index used when it is determined whether or not cells have differentiated in the second differentiation-inducing process, and is a value indicating a prescribed average area CAA. Also, the differentiation determination threshold value MTH3 is an index used when it is determined whether or not cells have differentiated in the third differentiation-inducing process, and is a value indicating a prescribed density CPT. 
     Here, preferably, the image determination device  10  determines whether or not cells have differentiated using an appropriate index among the differentiation determination threshold values MTH1 to MTH3 at an appropriate time in accordance with the degree of cell differentiation. The determination transition threshold value JTH is a threshold value indicating whether or not the transition to the determination based on the index (i.e., the differentiation determination threshold values MTH2 to MTH3) according to the next differentiation-inducing process is possible when a determination process based on an index (any one of the differentiation determination threshold values MTH1 to MTH3 in the present example) according to a certain differentiation-inducing process is performed in the differentiation-inducing process. The determination transition threshold value JTH1 is a threshold value indicating whether or not the transition to the determination based on the differentiation determination threshold value MTH2 in the second differentiation-inducing process is possible when the determination process based on the differentiation determination threshold value MTH1 is performed in the first differentiation-inducing process and is a value indicating a prescribed non-adhesion ratio UBR. The determination transition threshold value JTH2 is a threshold value indicating whether or not the transition to the determination based on the differentiation determination threshold value MTH3 in the third differentiation-inducing process is possible when the determination process based on the differentiation determination threshold value MTH2 is performed in the second differentiation-inducing process and is a value indicating a prescribed average area CAA. 
     That is, in the cell evaluation method using the image determination device  10 , when a magnitude relationship between the second index and the first index has changed, a second evaluation index and a third index calculated using the second evaluation index are acquired with respect to comparative target cells, a fourth index calculated using the second evaluation index is acquired with respect to evaluation target cells, and differentiation of the evaluation target cells is evaluated by comparing the third index with the fourth index. 
     Regarding Control Unit  100   
     The control unit  100  includes an acquisition unit  110 , a non-adhesion ratio-specific arithmetic unit  130 , an area-specific arithmetic unit  140 , and a density-specific arithmetic unit  150  as its functional units. The acquisition unit  110  acquires an image P captured by the imaging device  34 . The acquisition unit  110  supplies the acquired image P to the non-adhesion ratio-specific arithmetic unit  130 , the area-specific arithmetic unit  140 , and the density-specific arithmetic unit  150 . Specifically, the acquisition unit  110  supplies the first differentiation-inducing process image P1 among images P acquired from the imaging device  34  to the non-adhesion ratio-specific arithmetic unit  130 . The acquisition unit  110  supplies the second differentiation-inducing process image P2 among the images P acquired from the imaging device  34  to the area-specific arithmetic unit  140 . The acquisition unit  110  supplies the third differentiation-inducing process image P3 among the images P acquired from the imaging device  34  to the density-specific arithmetic unit  150 . 
     Regarding Non-Adhesion Ratio-Specific Arithmetic Unit  130   
     The non-adhesion ratio-specific arithmetic unit  130  includes a non-adhesion area calculation unit  131 , a non-adhesion ratio calculation unit  132 , and a non-adhesion ratio determination unit  133 . 
     The non-adhesion area calculation unit  131  acquires the first differentiation-inducing process image P1 from the acquisition unit  110 . The non-adhesion area calculation unit  131  extracts a non-adhesion portion image UBP, which is an image of the non-adhesion portion in which cells do not adhere to the culture vessel, from the acquired first differentiation-inducing process image P1. Also, the non-adhesion area calculation unit  131  extracts a culture medium image CMP, which is an image of the culture medium portion, from the acquired first differentiation-inducing process image P1. The non-adhesion area calculation unit  131  supplies the extracted non-adhesion portion image UBP and the culture medium image CMP to the non-adhesion ratio calculation unit  132 . 
     The non-adhesion ratio calculation unit  132  acquires the non-adhesion portion image UBP and the culture medium image CMP from the non-adhesion area calculation unit  131 . The non-adhesion ratio calculation unit  132  calculates a non-adhesion area UBS, which is an area of the non-adhesion portion where cells do not adhere to the culture vessel, on the basis of the acquired non-adhesion portion image UBP. Also, the non-adhesion ratio calculation unit  132  calculates an image-specific culture medium area CMS, which is an area of the culture medium contained in the captured image P, on the basis of the acquired culture medium image CMP. The non-adhesion ratio calculation unit  132  calculates the non-adhesion ratio UBR on the basis of the calculated non-adhesion area UBS and the calculated image-specific culture medium area CMS. In this example, a case where the non-adhesion ratio UBR is a value obtained by dividing the non-adhesion area UBS by the image-specific culture medium area CMS will be described. The non-adhesion ratio calculation unit  132  supplies the calculated non-adhesion ratio UBR to the non-adhesion ratio determination unit  133  and the area-specific arithmetic unit  140 . 
     The non-adhesion ratio determination unit  133  determines whether or not cells have differentiated in the first differentiation-inducing process. The non-adhesion ratio determination unit  133  acquires the non-adhesion ratio UBR from the non-adhesion ratio calculation unit  132 . The non-adhesion ratio determination unit  133  determines whether or not cells have differentiated in the first differentiation-inducing process on the basis of the acquired non-adhesion ratio UBR and the differentiation determination threshold value MTH1. In this example, the non-adhesion ratio determination unit  133  determines that cells have differentiated when the non-adhesion ratio UBR is less than the differentiation determination threshold value MT1. 
     The non-adhesion ratio determination unit  133  determines whether or not cells have differentiated after a point in time when the cells started to be cultured in the culture medium. That is, the non-adhesion ratio determination unit  133  determines whether or not cells have differentiated in the first differentiation-inducing process. Also, the non-adhesion ratio determination unit  133  ends the determination based on the non-adhesion ratio UBR and the differentiation determination threshold value MTH1 when the non-adhesion ratio UBR is less than the determination transition threshold value JTH1 on the basis of the non-adhesion ratio UBR and the determination transition threshold value JTH1. In other words, the non-adhesion ratio determination unit  133  determines whether or not cells have differentiated on the basis of the non-adhesion ratio UBR and the differentiation determination threshold value MTH1 until the non-adhesion ratio UBR is less than the determination transition threshold value JTH1 from the start of the cell culture process in the culture medium. 
     Hereinafter, the determination process of the non-adhesion ratio determination unit  133  will be described with reference to  FIG.  8   .  FIG.  8    is a graph showing an example of a determination process of the determination unit according to the first embodiment.  FIG.  8 (A)  shows a graph of a change over time in the non-adhesion ratio UBR. In  FIG.  8 (A) , the vertical axis represents a non-adhesion ratio UBR and the horizontal axis represents time. Also, time t0 here indicates a time when the cells started to be cultured in the culture medium. In  FIG.  8 (A) , a change over time in the non-adhesion ratio UBR is indicated by a waveform WUBR. Also, in  FIG.  8 (A) , change points of the non-adhesion ratio UBR according to time transition are referred to as time t11, time t12, and time t13, respectively. In this example, a case where the waveform WUBR indicating the non-adhesion ratio UBR shows a constant value from time t0 to time t11, decreases from time t12 to time t13, and shows a constant value at time t13 will be described. 
     As shown in  FIG.  8 (A) , the non-adhesion ratio calculation unit  132  determines whether or not cells in the first differentiation-inducing process have differentiated from time t0. Also, as shown in  FIG.  8 (A) , at time t12, the waveform WUBR indicating the non-adhesion ratio UBR calculated by the non-adhesion ratio calculation unit  132  is less than the differentiation determination threshold value MTH1. That is, at time t12, it is shown that the area where the cells adhere to the culture vessel is larger than that at the start of the culture process. That is, the non-adhesion ratio determination unit  133  determines that the cells in the first differentiation-inducing process have differentiated at time t12. 
     Also, as shown in  FIG.  8 (A) , at time t1, the waveform WUBR indicating the non-adhesion ratio UBR calculated by the non-adhesion ratio calculation unit  132  is less than the determination transition threshold value JTH1. That is, the non-adhesion ratio determination unit  133  determines whether or not cells have differentiated from time to to time t1. 
     Regarding Area-Specific Arithmetic Unit  140   
     Next, the area-specific arithmetic unit  140  will be described with reference to  FIG.  4   . The area-specific arithmetic unit  140  includes an area calculation unit  141 , an average area calculation unit  142 , and an area determination unit  143 . The area calculation unit  141  acquires the second differentiation-inducing process image P2 from the acquisition unit  110 . The area calculation unit  141  extracts a cell cluster image CCP, which is an image of a portion of a cell cluster CC, from the acquired second differentiation-inducing process image P2. In this example, the second differentiation-inducing process image P2 includes a plurality of imaged cell clusters CC. The area calculation unit  141  supplies the plurality of cell cluster images CCP that have been extracted to the average area calculation unit  142 . 
     The average area calculation unit  142  acquires a plurality of cell cluster images CCP from the area calculation unit  141 . The average area calculation unit  142  calculates a cell cluster area CSS, which is an area of the cell cluster CC, on the basis of the plurality of cell cluster images CCP that have been acquired. In this example, the average area calculation unit  142  calculates an average area CAA, which is an average value of a plurality of cell cluster areas CSS calculated on the basis of a plurality of cell cluster images CCP that have been acquired. The average area calculation unit  142  supplies the calculated average area CAA to the area determination unit  143  and the density-specific arithmetic unit  150 . 
     The area determination unit  143  determines whether or not cells have differentiated in the second differentiation-inducing process. Specifically, the area determination unit  143  acquires the average area CAA from the average area calculation unit  142 . The area determination unit  143  determines whether or not cells have differentiated in the second differentiation-inducing process on the basis of the acquired average area CAA and the differentiation determination threshold value MTH2. In this example, the area determination unit  143  determines that cells have differentiated when the average area CAA is less than the differentiation determination threshold value MTH2. 
     The area-specific arithmetic unit  140  acquires the non-adhesion ratio UBR from the non-adhesion ratio calculation unit  132 . The area calculation unit  141  starts the calculation of the average area CAA when the non-adhesion ratio UBR is less than the determination transition threshold value JTH1. The area determination unit  143  starts the determination after the non-adhesion ratio determination unit  133  ends the determination process for the cells based on the non-adhesion ratio UBR and the differentiation determination threshold value MTH1. Also, the area determination unit  143  determines whether or not cells have differentiated on the basis of the calculated average area CAA and the differentiation determination threshold value MTH2. Also, when the calculated average area CAA is less than the determination transition threshold value JTH2, the area determination unit  143  ends the determination process for the cells based on the average area CAA and the differentiation determination threshold value MTH2. That is, the area determination unit  143  determines whether or not cells have differentiated on the basis of the average area CAA and the differentiation determination threshold value MTH2 until the average area CAA is less than the determination transition threshold value JTH2 after the transition from the first differentiation-inducing process to the second differentiation-inducing process. 
     Hereinafter, the determination process of the area determination unit  143  will be described with reference to  FIG.  8   . In  FIG.  8 (B) , a graph of a change over time in the average area CAA is shown. In  FIG.  8 (B) , the vertical axis represents an average area CAA and the horizontal axis represents time. Also, in  FIG.  8 (B) , a change over time in the average area CAA is indicated by a waveform WCAA. Also, in  FIG.  8 (B) , change points of the average area CAA due to the time transition are referred to as time t21, time t22, and time t23, respectively. In this example, a case where the waveform WCAA indicating the average area CAA shows a constant value from time t0 to time t21, decreases from time t22 to time t23, and shows a constant value at time t23 will be described. 
     As shown in  FIG.  8 (B) , the area determination unit  143  determines whether or not cells in the second differentiation-inducing process have differentiated from time t1. Also, as shown in  FIG.  8 (B) , at time t22, the average area CAA calculated by the average area calculation unit  142  is less than the differentiation determination threshold value MTH2. That is, at time t22, it is shown that the cell cluster area CSS is smaller than that at the start of the culture process. That is, the area determination unit  143  determines that cells in the second differentiation-inducing process have differentiated at time t22. 
     Also, as shown in  FIG.  8 (B) , at time t2, the waveform WCAA indicating the average area CAA calculated by the area determination unit  143  is less than the determination transition threshold value JTH2. That is, the area determination unit  143  determines whether or not cells have differentiated between time t1 and time t2. 
     Regarding Density-Specific Arithmetic Unit  150   
     Next, the density-specific arithmetic unit  150  will be described with reference to  FIG.  4   . The density-specific arithmetic unit  150  includes an area/perimeter calculation unit  151 , a density calculation unit  152 , and a density determination unit  153 . 
     The area/perimeter calculation unit  151  acquires the third differentiation-inducing process image P3 from the acquisition unit  110 . The area/perimeter calculation unit  151  extracts a portion of the cell image CP from the acquired third differentiation-inducing process image P3. In this example, the third differentiation-inducing process image P3 includes a plurality of imaged cells. The area/perimeter calculation unit  151  supplies a plurality of pieces of information indicating the extracted cell image CP to the density calculation unit  152 . 
     The density calculation unit  152  acquires a plurality of cell images CP from the area/perimeter calculation unit  151 . The density calculation unit  152  calculates a cell area CS on the basis of the plurality of cell images CP that have been acquired. Also, the density calculation unit  152  calculates perimeters of cells on the basis of the acquired plurality of cell images CP and calculates an average cell perimeter CL on the basis of the calculated perimeters of the cells. The density calculation unit  152  calculates a ratio of the average cell perimeter CL to the cell area CS for each acquired cell image CP. Also, the density calculation unit  152  calculates a density CPT, which is an average value of the ratio of the average cell perimeter CL to the cell area CS calculated for each cell image CP. The density calculation unit  152  supplies the calculated density CPT to the density determination unit  153 . 
     The density determination unit  153  determines whether or not cells have differentiated in the third differentiation-inducing process. Specifically, the density determination unit  153  acquires the density CPT from the density calculation unit  152 . The density determination unit  153  determines whether or not cells have differentiated in the third differentiation-inducing process on the basis of the acquired density CPT and the differentiation determination threshold value MTH3. In this example, the density determination unit  153  determines that cells have differentiated when the density CPT is less than the differentiation determination threshold value MTH3. 
     The density-specific arithmetic unit  150  acquires the average area CAA from the average area calculation unit  142 . The density-specific arithmetic unit  150  starts the calculation when the average area CAA is less than the determination transition threshold value JTH2. The density determination unit  153  starts the determination after the transition from the second differentiation-inducing process to the third differentiation-inducing process. That is, the density determination unit  153  starts the determination from the time when the area determination unit  143  has ended the determination. 
     Hereinafter, the determination process of the density determination unit  153  will be described with reference to  FIG.  8 (C) . In  FIG.  8 (C) , a graph of a change over time in the density CPT is shown. In  FIG.  8 (C) , the vertical axis represents a density CPT and the horizontal axis represents time. Also, in  FIG.  8 (C) , a change over time in the density CPT is indicated by a waveform WCPT. Also, in  FIG.  8 (C) , change points due to the time transition in the density CPT are referred to as time t31 and time  32 , respectively. In this example, a case where the waveform WCPT indicating the density CPT gradually decreases from time t0 to time t32 and shows a constant value at time t32 will be described. 
     As shown in  FIG.  8 (C) , the density determination unit  153  determines whether or not cells in the third differentiation-inducing process have differentiated from time t1. Also, as shown in  FIG.  8 (C) , at time  31 , the waveform WCPT indicating the density CPT calculated by the density calculation unit  152  is less than the differentiation determination threshold value MTH3. Here, when cells have various shapes, gaps may occur between the cells. On the other hand, when the shape of the cells becomes uniform (for example, circular), gaps between the cells are unlikely to occur (i.e., the density CPT becomes high). In other words, when the density CPT becomes high, the cell shape may be close to a circle. That is, it is shown that the shape of the cells is closer to a circle at time t31 than at the start of the culture process. That is, the density determination unit  153  determines that cells in the third differentiation-inducing process have differentiated at time  31 . 
     Hereinafter, an operation of the image determination device  10  will be described with reference to  FIG.  9   .  FIG.  9    is a flowchart (Part  1 ) showing an example of the operation of the image determination device  10  according to the first embodiment  FIG.  10    is a flowchart (Part  2 ) showing an example of the operation of the image determination device  10  according to the first embodiment.  FIG.  11    is a flowchart (Part  3 ) showing an example of the operation of the image determination device  10  according to the first embodiment. The acquisition unit  110  acquires an image P from the imaging device  34  (step S 100 ). The acquisition unit  110  supplies the image P to each part provided in the control unit  100  (step S 110 ). 
     The non-adhesion area calculation unit  131  provided in the non-adhesion ratio-specific arithmetic unit  130  acquires a first differentiation-inducing process image P1 from among a plurality of images P (the first differentiation-inducing process image P1, the second differentiation-inducing process image P2, and the third differentiation-inducing process image P3) in each differentiation step acquired by the acquisition unit  110  from the imaging device  34  (step S 120 ). The non-adhesion area calculation unit  131  extracts a non-adhesion portion image UBP from the first differentiation-inducing process image P1 (step S 130 ). The non-adhesion area calculation unit  131  extracts a culture medium image CMP from the first differentiation-inducing process image P1 (step S 140 ). The non-adhesion area calculation unit  131  supplies the non-adhesion portion image UBP and the culture medium image CMP to the non-adhesion ratio calculation unit  132  (step S 150 ). The non-adhesion ratio calculation unit  132  provided in the non-adhesion ratio-specific arithmetic unit  130  acquires the non-adhesion portion image UBP and the culture medium image CMP from the non-adhesion area calculation unit  131  (step S 160 ). The non-adhesion ratio calculation unit  132  calculates an image-specific culture medium area CMS on the basis of the acquired culture medium image CMP (step S 170 ). The non-adhesion ratio calculation unit  132  calculates the non-adhesion area UBS on the basis of the acquired non-adhesion portion image UBP (step S 180 ). The non-adhesion ratio calculation unit  132  calculates anon-adhesion ratio UBR on the basis of the calculated image-specific culture medium area CMS and the non-adhesion area UBS (step S 190 ). The non-adhesion ratio calculation unit  132  supplies the calculated non-adhesion ratio UBR to the non-adhesion ratio determination unit  133  and the area-specific arithmetic unit  140  (step S 200 ). 
     The non-adhesion ratio determination unit  133  provided in the non-adhesion ratio-specific arithmetic unit  130  acquires the non-adhesion ratio UBR from the non-adhesion ratio calculation unit  132  (step S 210 ). The non-adhesion ratio determination unit  133  reads the determination transition threshold value JTH1 from the storage unit  300  (step S 220 ). 
     The non-adhesion ratio determination unit  133  determines whether or not the non-adhesion ratio UBR is less than the determination transition threshold value JTH1 (step S 230 ). When it is determined that the non-adhesion ratio UBR is less than the determination transition threshold value JTH1 (step S 230 ; YES), the non-adhesion ratio determination unit  133  ends the process without making the determination. Also, when it is determined that the non-adhesion ratio UBR exceeds the determination transition threshold value JTH1 (step S 230 ; NO), the non-adhesion ratio determination unit  133  reads the differentiation determination threshold value MTH1 from the storage unit  300  (step S 240 ). The non-adhesion ratio determination unit  133  determines whether or not the non-adhesion ratio UBR is less than the differentiation determination threshold value MTH1 (step S 250 ). When it is determined that the non-adhesion ratio UBR is less than the differentiation determination threshold value MTH1 (step S 250 ; YES), the non-adhesion ratio determination unit  133  determines that cells have differentiated in the first differentiation-inducing process (step S 260 ). Also, when it is determined that the non-adhesion ratio UBR exceeds the differentiation determination threshold value MTH1 (step S 250 ; NO), the non-adhesion ratio determination unit  133  determines that cells have not differentiated in the first differentiation-inducing process (step S 270 ). 
     The area-specific arithmetic unit  140  acquires the non-adhesion ratio UBR from the non-adhesion ratio calculation unit  132  (step S 280 ). The area-specific arithmetic unit  140  reads the determination transition threshold value JTH1 from the storage unit  300  (step S 290 ). The area-specific arithmetic unit  140  determines whether or not the non-adhesion ratio UBR is less than the determination transition threshold value JTH1 (step S 300 ). When it is determined that the non-adhesion ratio UBR is less than the determination transition threshold value JTH1 (step S 300 ; YES), the area-specific arithmetic unit  140  moves the process to step S 310 . The area calculation unit  141  does not start the process while it is determined that the non-adhesion ratio UBR exceeds the determination transition threshold value JTH1 (step S 300 ; NO). The area calculation unit  141  provided in the area-specific arithmetic unit  140  acquires an image P acquired (captured) at a timing after an image P in which it is determined that the non-adhesion ratio UBR is less than the determination transition threshold value JTH1 among the plurality of images P acquired by the acquisition unit  110  as the second differentiation-inducing process image P2 (step S 310 ). The area calculation unit  141  extracts a plurality of cell cluster images CCP from the acquired second differentiation-inducing process image P2 (step S 320 ). The area calculation unit  141  supplies information indicated by the plurality of cell cluster images CCP that have been extracted to the average area calculation unit  142  (step S 330 ). 
     The average area calculation unit  142  provided in the area-specific arithmetic unit  140  acquires a plurality of cell cluster images CCP from the area calculation unit  141  (step S 340 ). The average area calculation unit  142  calculates a cell cluster area CSS on the basis of the plurality of cell cluster images CCP that have been acquired (step S 350 ). The average area calculation unit  142  calculates an average area CAA on the basis of the calculated cell cluster area CSS (step S 360 ). The average area calculation unit  142  supplies the calculated average area CAA to the area determination unit  143  and the density-specific arithmetic unit  150  (step S 370 ). 
     The area determination unit  143  provided in the area-specific arithmetic unit  140  acquires the average area CAA from the average area calculation unit  142  (step S 380 ). The area determination unit  143  reads the determination transition threshold value JTH2 from the storage unit  300  (step S 390 ). The area determination unit  143  determines whether or not the average area CAA is less than the determination transition threshold value JTH2 (step S 400 ). When the area determination unit  143  determines that the average area CAA is not less than the determination transition threshold value JTH2 (step S 400 ; NO), the area determination unit  143  reads the differentiation determination threshold value MTH2 from the storage unit  300  (step S 410 ). Also, when it is determined that the average area CAA is less than the determination transition threshold value JTH2 (step S 400 ; YES), the area determination unit  143  ends the process without making the determination. The area determination unit  143  reads the differentiation determination threshold value MTH2 from the storage unit  300  (step S 410 ). The area determination unit  143  determines whether or not the average area CAA is less than the differentiation determination threshold value MTH2 (step S 420 ). When it is determined that the average area CAA is less than the differentiation determination threshold value MTH2 (step S 420 ; YES), the area determination unit  143  determines that cells have differentiated in the second differentiation-inducing process (step S 440 ). Also, when it is determined that the average area CAA exceeds the differentiation determination threshold value MTH2 (step S 420 ; NO), the area determination unit  143  determines that cells have not differentiated in the second differentiation-inducing process (step S 430 ). 
     The density-specific arithmetic unit  150  acquires the average area CAA from the average area calculation unit  142  (step S 450 ). The density-specific arithmetic unit  150  reads the determination transition threshold value JTH2 from the storage unit  300  (step S 460 ). The density-specific arithmetic unit  150  determines whether or not the average area CAA is less than the determination transition threshold value JTH2 (step S 470 ). When it is determined that the average area CAA is less than the determination transition threshold value JTH2 (step S 470 ; YES), the density-specific arithmetic unit  150  moves the process to step S 480 . The density-specific arithmetic unit  150  does not start the process while it is determined that the average area CAA exceeds the determination transition threshold value JTH2 (step S 470 ; NO). The area/perimeter calculation unit  151  provided in the density-specific arithmetic unit  150  acquires an image P acquired (captured) at a timing after an image P in which it is determined that the average area CAA is less than the determination transition threshold value JTH2 among the plurality of images P acquired by the acquisition unit  110  as the third differentiation-inducing process image P3 (step S 480 ). The area/perimeter calculation unit  151  extracts a plurality of cell images CP from the acquired third differentiation-inducing process image P3 (step S 490 ). The area/perimeter calculation unit  151  supplies a plurality of cell images CP that have been extracted to the density calculation unit  152  (step S 500 ). 
     The density calculation unit  152  provided in the density-specific arithmetic unit  150  acquires the plurality of cell images CP from the area/perimeter calculation unit  151  (step S 510 ). The density calculation unit  152  calculates an average cell perimeter CL on the basis of the plurality of cell images CP that have been acquired (step S 520 ). The density calculation unit  152  calculates a cell area CS on the basis of the plurality of cell images CP that have been acquired (step S 530 ). Also, the density calculation unit  152  calculates the average cell perimeter CL on the basis of the plurality of cell images CP that have been acquired (step S 540 ). The density calculation unit  152  calculates a density CPT on the basis of the calculated cell area CS and the average cell perimeter CL (step S 540 ). The density calculation unit  152  supplies the calculated density CPT to the density determination unit  153  (step S 550 ). 
     The density calculation unit  152  provided in the density-specific arithmetic unit  150  acquires the density CPT from the density calculation unit  152  (step S 560 ). The density determination unit  153  reads the differentiation determination threshold value MTH3 from the storage unit  300  (step S 570 ). The density determination unit  153  determines whether or not the density CPT is less than the differentiation determination threshold value MTH3 (step S 580 ). When it is determined that the density CPT is less than the differentiation determination threshold value MTH3 (step S 580 ; YES), the density determination unit  153  determines that cells have differentiated in the third differentiation-inducing process (step S 600 ). Also, when it is determined that the density CPT exceeds the differentiation determination threshold value MTH3 (step S 580 ; NO), the density determination unit  153  determines that cells have not differentiated in the third differentiation-inducing process (step S 590 ). Subsequently, the image determination device  10  iterates the processing of steps S 100  to S 600  at a user&#39;s input or at a prescribed timing. 
     As described above, the image determination device  10  includes an acquisition unit  110 , a non-adhesion ratio-specific arithmetic unit  130 , an area-specific arithmetic unit  140 , and a density-specific arithmetic unit  150 . The non-adhesion ratio-specific arithmetic unit  130  includes a non-adhesion ratio calculation unit  132  and a non-adhesion ratio determination unit  133 . The area-specific arithmetic unit  140  includes an average area calculation unit  142  and an area determination unit  143 . The density-specific arithmetic unit  150  includes the density calculation unit  152  and the density determination unit  153 . The acquisition unit  110  acquires a plurality of images P obtained by the imaging device  34  imaging cells being cultured in the culture medium according to the elapse of time. The non-adhesion ratio-specific arithmetic unit  130 , the area-specific arithmetic unit  140 , and the density-specific arithmetic unit  150  determine whether or not cells have differentiated in each differentiation-inducing process on the basis of various types of information. 
     Thereby, the image determination device  10  can determine whether or not cells have differentiated on the basis of an image P obtained by imaging the cells being culture, a plurality of calculation units, and a plurality of determination units. That is, the image determination device  10  can determine whether or not cells have differentiated on the basis of an index according to each differentiation-inducing process of the first differentiation-inducing process, the second differentiation-inducing process, and the third differentiation-inducing process. Thereby, the image determination device  10  can reduce the time and effort for determining a cell culture state on the basis of information according to the cell differentiation-inducing process. 
     Also, the non-adhesion ratio calculation unit  132  calculates a non-adhesion ratio UBR on the basis of the image-specific culture medium area CMS and the non-adhesion area UBS. The average area calculation unit  142  calculates an average area CAA on the basis of the cell cluster area CSS. The density calculation unit  152  calculates a density CPT on the basis of the cell area CS and the average cell perimeter CL. Thereby, the image determination device  10  can determine whether or not cells have differentiated on the basis of indices according to the differentiation-inducing processes calculated by the plurality of calculation units. That is, the image determination device  10  can determine whether or not cells have differentiated on the basis of an index according to each differentiation-inducing process of the first differentiation-inducing process, the second differentiation-inducing process, and the third differentiation-inducing process. 
     Also, the area determination unit  143  provided in the area-specific arithmetic unit  140  starts the determination on the basis of the fact that the non-adhesion ratio UBR calculated by the non-adhesion ratio calculation unit  132  is less than the determination transition threshold value JTH1. Thereby, the image determination device  10  can determine whether or not cells have differentiated on the basis of an index according to the transition from the first differentiation-inducing process to the second differentiation-inducing process. That is, the image determination device  10  can determine whether or not cells have differentiated on the basis of indices according to the first differentiation-inducing process and the second differentiation-inducing process. 
     Also, the density-specific arithmetic unit  150  starts the determination on the basis of the fact that the average area CAA calculated by the average area calculation unit  142  is less than the determination transition threshold value JTH2. Thereby, the image determination device  10  can determine whether or not cells have differentiated on the basis of an index according to the transition from the second differentiation-inducing process to the third differentiation-inducing process. That is, the image determination device  10  can determine whether or not cells have differentiated on the basis of indices according to the second differentiation-inducing process and the third differentiation-inducing process. 
     Second Embodiment 
     Hereinafter, a second embodiment of the present invention will be described with reference to  FIG.  12   .  FIG.  12    is a graph showing an example of a determination process of a determination unit according to the second embodiment. Components and operations that are similar to those of the first embodiment described above are denoted by the same reference signs and description thereof will be omitted. In the present embodiment, a case where determination processes of the non-adhesion ratio determination unit  133 , the area determination unit  143 , and the density determination unit  153  are started on the basis of the elapsed time of culturing of cells will be described. Specifically, during a period from time t0 to time t100, which is a prescribed time, the non-adhesion ratio determination unit  133  determines whether or not cells in a first differentiation-inducing process have differentiated on the basis of a non-adhesion ratio UBR calculated by the non-adhesion ratio calculation unit  132  and a differentiation determination threshold value MTH1. Also, the non-adhesion ratio determination unit  133  ends the determination at time t100. Also, during a period from time t100 to time t110, which is a prescribed time, the area determination unit  143  determines whether or not cells in a second differentiation-inducing process have differentiated on the basis of an average area CAA calculated by the average area calculation unit  142  and a differentiation determination threshold value MTH2. Also, the area determination unit  143  ends the determination at time t110. Also, after time t110, the density determination unit  153  determines whether or not cells in a third differentiation-inducing process have differentiated on the basis of a density CPT calculated by the density calculation unit  152  and a differentiation determination threshold value MTH3. 
     As described above, the non-adhesion ratio determination unit  133 , the area determination unit  143 , and the density determination unit  153  provided in the non-adhesion ratio-specific arithmetic unit  130 , the area-specific arithmetic unit  140 , and the density-specific arithmetic unit  150  start the determination processes on the basis of the elapsed time of culturing of cells. Thereby, the image determination device  10  can determine whether or not cells have differentiated on the basis of an index according to the transition to each differentiation-inducing process at the time of culturing of cells for which a period of each differentiation-inducing process is already clear. That is, the image determination device  10  can determine whether or not cells have differentiated on the basis of the index according to each differentiation-inducing process. 
     In a cell evaluation method using the image determination device  10  according to the present embodiment, a first evaluation index based on the elapsed time from the start of the culture process is acquired. 
     Modified Example 1 
     Hereinafter, Modified Example 1 of the first embodiment described above will be described with reference to  FIG.  13   .  FIG.  13    is a graph showing an example of a determination process of the determination unit in Modified Example 1. Components and operations that are similar to those of the first embodiment described above are denoted by the same reference signs and description thereof will be omitted. 
     In Modified Example 1, a case where the determination times of the non-adhesion ratio determination unit  133 , the area determination unit  143 , and the density determination unit  153  overlap will be described. 
     In this example, a case where two threshold values of a determination transition threshold value JTH2-1 and a determination transition threshold value JTH2-2 for a determination transition threshold value JTH indicating the end of the determination process of the area determination unit  143  and the start of the determination process of the density determination unit  153  are set will be described. In this case, as shown in  FIG.  13   , time t2-1 is a time when the area determination unit  143  determines that the average area CAA is less than the determination transition threshold value JTH2-1. Time t2-2 is a time when it is determined that the average area CAA is less than the determination transition threshold value JTH2-2. In the image determination device  10 , during a period from time t2-1 to time t2-2, the area determination unit  143  may determine whether or not cells have differentiated and the density determination unit  153  may determine whether or not cells have differentiated. 
     In the above-described first embodiment, after a result of determining the average area CAA in the area-specific arithmetic unit  140  indicates that cells have differentiated, a process of determining the density CPT in the density-specific arithmetic unit  150  is started. Thus, when the result of determining the average area CAA in the area-specific arithmetic unit  140  indicates that cells have not differentiated, a process of determining the density CPT in the density-specific arithmetic unit  150  may not be started. Therefore, in the above-described first embodiment, the result of determining the density CPT in the density-specific arithmetic unit  150  may not be obtained according to a state of the cell cluster image CCP. 
     On the other hand, in Modified Example 1, the process of determining the density CPT in the density-specific arithmetic unit  150  is started regardless of the result of determining the average area CAA in the area-specific arithmetic unit  140 . Therefore, in Modified Example 1, a result of determining the density CPT in the density-specific arithmetic unit  150  can be obtained regardless of the state of the cell cluster image CCP. That is, according to Modified Example 1, it is possible to reduce a frequency with which the result of determining the density CPT in the density-specific arithmetic unit  150  cannot be obtained. 
     Although the case where the determination times set by the determination transition threshold value JTH overlap has been described in this example, the present invention is not limited thereto. In the determination processes of the non-adhesion ratio determination unit  133 , the area determination unit  143 , and the density determination unit  153 , determination periods may overlap according to the elapsed time of culturing of cells. Specifically, time t2-2 at which the determination process of the area determination unit  143  ends and time t2-1 at which the determination process of the density determination unit  153  starts may be set on the basis of the elapsed time of culturing of cells. 
     Modified Example 2 
     Hereinafter, Modified Example 2 of each of the above-described embodiments will be described with reference to  FIG.  14   .  FIG.  14    is a schematic diagram showing an example of a configuration of the image determination device in Modified Example 2. Components and operations that are similar to those of each embodiment described above are denoted by the same reference signs and description thereof will be omitted. In Modified Example 2, an image P acquired by the image determination device  10  from the imaging device  34  may be an image P obtained by imaging cells in any differentiation-inducing process. In this example, the non-adhesion ratio-specific arithmetic unit  130 , the area-specific arithmetic unit  140 , and the density-specific arithmetic unit  150  calculate indices in parallel on the basis of the image P supplied by the acquisition unit  110 . Specifically, the non-adhesion area calculation unit  131  and the non-adhesion ratio calculation unit  132  calculate a non-adhesion ratio UBR on the basis of the image P supplied by the acquisition unit  110 . Also, the area calculation unit  141  and the average area calculation unit  142  calculate an average area CAA on the basis of the image P supplied by the acquisition unit  110 . Also, the area/perimeter calculation unit  151  and the density calculation unit  152  calculate a density CPT on the basis of the image P supplied by the acquisition unit  110 . The non-adhesion ratio determination unit  133 , the area determination unit  143 , and the density determination unit  153  perform the determination processes when conditions are satisfied. Specifically, when the non-adhesion ratio UBR exceeds the determination transition threshold value JTH1, the non-adhesion ratio determination unit  133  determines whether or not cells have differentiated on the basis of the non-adhesion ratio UBR. Also, the area determination unit  143  determines whether or not cells have differentiated on the basis of the average area CAA when the non-adhesion ratio UBR is less than the determination transition threshold value JTH1 and when the average area CAA exceeds the determination transition threshold value JTH2. Also, when the average area CAA is less than the determination transition threshold value JTH2, the density determination unit  153  determines whether or not cells have differentiated on the basis of the density CPT. 
     Thereby, even if the image P captured by the imaging device  34  is any one of the first differentiation-inducing process image P1, the second differentiation-inducing process image P2, and the third differentiation-inducing process image P3, it is possible to determine whether or not cells have differentiated. 
     Third Embodiment 
     Hereinafter, a third embodiment of the present invention will be described with reference to  FIGS.  15  to  34   . In the above-described embodiments and modified examples, a configuration in which it is determined whether or not cells have differentiated on the basis of each differentiation-inducing process-specific index according to each differentiation-inducing process has been described. In the third embodiment, a configuration in which the quality of cell differentiation is determined using at least one common index regardless of the differentiation-inducing process will be described. Also, determining the quality of cell differentiation can be rephrased as evaluating cell differentiation and can be rephrased as determining the quality of cell maturation or evaluating cell maturation because differentiation is also a cell maturation process. Components and operations that are similar to those of the above-described embodiments and modified examples are denoted by the same reference signs and description thereof will be omitted. In the third embodiment, an undifferentiated state maintaining culture process in which cells are maintained and cultured in an undifferentiated state and a culture process of differentiating cells into mature hepatocytes among culture processes of differentiating iPS cell-derived endoderm cells into mature cells will be described as an example. 
     Even in the third embodiment, the mature cells are not limited to mature hepatocytes. The mature cells in the third embodiment are cells in which a paving stone region (to be described below) appears in a process of normal differentiation. Examples of such cells include adhesive epithelial cells derived from liver, ovary, skin, cornea, various types of digestive organs, tracheas, and various other types of mucous membranes. 
       FIG.  15    is a schematic diagram showing an example of a culture process until differentiation of iPS cells into mature hepatocytes is performed according to the third embodiment. The undifferentiated state maintaining culture process for iPS cells is a culture process (for example, 5 days) for increasing the number of iPS cells while maintaining the iPS cells in an undifferentiated state. The undifferentiated state maintaining culture process is performed during a period before the culture process for differentiation into mature hepatocytes. In the present embodiment, the undifferentiated state maintaining culture process is performed for days −5 to 0 (i.e., −120 to 0 hours) after the iPS cells are cultured in the culture medium. A first differentiation-inducing process in which iPS cells are induced to differentiate into endoderm cells is performed for days 1 to 7 (i.e., 0 to 168 hours) after the iPS cells are cultured in the culture medium. Also, a second differentiation-inducing process in which endoderm cells are induced to differentiate into hepatic endoderm cells is performed for days 8 to 10 (i.e., 169 to 240 hours) after the cell culture process is started. Also, a third differentiation-inducing process in which endoderm cells are induced to differentiate into hepatic endoderm cells is performed for days 11 to 14 (i.e., 241 to 336 hours) after the cell culture process is started. Also, a fourth differentiation-inducing process in which hepatic endoderm cells are induced to differentiate into mature hepatocytes is performed for days 15 to 21 (i.e., 337 to 504 hours) after the cell culture process is started. 
     The image determination device  10  determines the quality of cell differentiation on the basis of six evaluation indices obtained from images (i.e., time-lapse images of cells) acquired during the first to fourth differentiation-inducing processes (i.e., days 1 to 21). As shown in  FIG.  15   , the six evaluation indices are an average value of luminance values in the paving stone region (an index ID10), an area of the cell adhesion region (an index ID11), an area of the adhesion region (an index ID12), an area of the non-cell region (an index ID13), an area of the dying and dead cell region (an index ID14), an area of the dead cell region (an index ID15). 
     Also, the time-lapse images of the cells are images of cells captured in time series. The time-lapse images include at least two or more images captured at different times. Hereinafter, the configuration of the image determination device  10  will be described. 
       FIG.  16    is a diagram showing an example of a configuration of the image determination device  10  of the third embodiment. As shown in  FIG.  16   , the imaging device  34  and the image determination device  10  are connected so that they can transmit and receive information. The imaging device  34  images the cells being cultured in the culture medium according to the elapse of time. Specifically, the imaging device  34  captures an image P, which is an image of cells containing the culture medium, according to the elapse of time. The imaging device  34  captures an image of the cells and supplies the image P that has been generated to the image determination device  10 . 
     As shown in  FIG.  16   , the image determination device  10  of the third embodiment includes a control unit  101  and a storage unit  301 . The control unit  101  implements an acquisition unit  110 , a paving stone region extraction unit  160 , a paving stone region-specific average luminance value calculation unit  162 , an adhesion region extraction unit  170 , an adhesion region area calculation unit  172 , an cell adhesion region extraction unit  180 , a cell adhesion region area calculation unit  182 , a non-cell region extraction unit  190 , a non-cell region area calculation unit  192 , a dead cell region extraction unit  200 , a dead cell region area calculation unit  202 , a dying and dead cell region extraction unit  210 , a dying and dead cell region area calculation unit  212 , an effect quantity calculation unit  219 , a determination unit  220 , and an output unit  230  as functional units when a hardware processor such as a CPU executes a program (software) stored in the storage unit  301 . Some or all of these components may be implemented by hardware such as an LSI circuit, an ASIC, an FPGA, or a GPU or may be implemented by software and hardware in cooperation. The storage unit  301  is implemented by, for example, a ROM, a flash memory, an SD card, a RAM, a register, or the like. The storage unit  301  stores various types of values such as a paving stone region-specific average luminance threshold value  301   a , an adhesion region area-specific threshold value  301   b , a cell adhesion region area-specific threshold value  301   c , a non-cell region area-specific threshold value  301   d , a dead cell region area-specific threshold value  301   e , and a dying and dead cell region area-specific threshold value  301   f  in advance. 
     These threshold values are values predetermined by comparing indices (for example, a paving stone region luminance value, an adhesion region area, a cell adhesion region area, a non-cell region area, a photoreceptor cell region area, a dying and dead cell region area, and the like) based on an image P for which the observer observes cells and determines that the observed cells have become normal mature hepatocytes with indices (for example, a paving stone region luminance value, an adhesion region area, a cell adhesion region area, a non-cell region area, a photoreceptor cell region area, a dying and dead cell region area, and the like) based on an image P for which the observer observes cells and determines that the observed cells have not become normal mature hepatocytes (for example, that the observed cells have not become mature hepatocytes, that the observed cells have become abnormal mature hepatocytes, or the like). Details of information will be described below. 
     Also, the observer may not determine whether or not cells have become mature hepatocytes by observing the cells. The observer may make the determination, for example, using existing biochemical indices. In this case, for example, it may be determined whether or not the hepatocytes have become mature hepatocytes in an enzyme-linked immunosorbent assay (ELISA) method. 
     The acquisition unit  110  of the present embodiment supplies the image P acquired from the imaging device  34  to the paving stone region extraction unit  160 , the adhesion region extraction unit  170 , the cell adhesion region extraction unit  180 , the non-cell region extraction unit  190 , the dead cell region extraction unit  200 , and the dying and dead cell region extraction unit  210 . 
     &lt;Regarding Paving Stone Region&gt; 
     The paving stone region extraction unit  160  extracts a paving stone-shaped region from the image P.  FIG.  17    is a diagram showing an example of cells having a paving stone-shaped form. As shown in  FIG.  17   , the contrast between the cell membrane of the cell and the inside of the cell membrane (i.e., cytoplasm, nucleus, or the like) increases as the differentiation of iPS cells into hepatic endoderm cells progresses. The cells (hepatic endoderm cells) are arranged while adhering to the culture vessel as if the cells are laid out with paving stones and spread in the culture medium. In the following description, a paving stone laying pattern in which the cells spread in the culture medium while adhering to the culture vessel is also referred to as a paving stone shape. The paving stone laying pattern is, for example, a pattern in which plane figures having irregular sizes and shapes are laid out in a plane with almost no gap between them. Also, cells that form the paving stone laying pattern are also referred to as “cells having a paving stone-shaped form.” The paving stone laying pattern may be formed by one “cell having a paving stone-shaped form” or may be formed by a plurality of “cells having a paving stone-shaped form.” Also, it can be said that the cells having a paving stone-shaped form are cells in which differentiation from iPS cells is normally progressing. The paving stone region extraction unit  160  extracts a region (in other words, a cell membrane of a cell having the paving stone-shaped form and a cytoplasmic region of the cell) of the image P surrounded by a contour of outer circumference of a cell having the paving stone-shaped form as a paving stone-shaped region (hereinafter referred to as a paving stone region). Also, the paving stone region extraction unit  160  may extract a region of the image P surrounded by a contour of outer circumference around which a plurality of cells having a paving stone-shaped form are gathered or may further extract a set of pixels of a plurality of paving stone regions within the image P as the paving stone region. 
     Also, the paving stone region extraction unit  160  determines whether or not cells have formed the paving stone region. Here, the paving stone region extraction unit  160  determines whether or not cells have formed a paving stone region on the basis of, for example, an average value obtained by averaging differences between luminance values in the cytoplasm of the cells imaged in the image P and luminance values of the cell membranes thereof with respect to all cells imaged in the image P. Also, it may be determined whether or not cells have formed the paving stone region on the basis of an average luminance value of pixels in a region where a cell form shows a prescribed feature among the plurality of cells imaged in the image P. The paving stone region extraction unit  160  is an example of a paving stone region determination unit. 
     The paving stone region-specific average luminance value calculation unit  162  calculates an average value of luminance values in pixels indicating a paving stone region extracted by the paving stone region extraction unit  160 . The paving stone region-specific average luminance value calculation unit  162  may calculate a median value of the luminance values instead of (or in addition to) the configuration in which the average value of the luminance values is calculated, may calculate the most frequent value of the luminance values, may calculate variance of the luminance values, may calculate a coefficient of variation of the luminance value (a value by dividing standard deviation of the luminance value by the average value of the luminance values), may calculate an average value, a median value, variance, a coefficient of variation, and the like with respect to a luminance value of a paving stone region having an area greater than or equal to the threshold value, or may calculate an area of the paving stone region. In the following description, a case where the paving stone region-specific average luminance value calculation unit  162  calculates an average value of luminance values in pixels showing the paving stone region will be described as an example and the average value will also be referred to as an “average value of luminance values in the paving stone region.” 
     &lt;Regarding Adhesion Region&gt; 
     The adhesion region extraction unit  170  extracts an adhesion region from the image P. As described above, iPS cells adhere to a culture vessel as the differentiation-inducing process for hepatic endoderm cells progresses. Here, cells having the above-described paving stone-shaped form, cells whose quality of differentiation is low, cells that are undergoing differentiation, or impurities in the cell culture process may adhere to the culture vessel. The impurities in the cell culture process include, for example, garbage generated when the culture medium is replaced, cell residue generated when cells are crushed, and the like. The adhesion region extraction unit  170  extracts a region of cells having a paving stone-shaped form (i.e., a paving stone region), a region of cells whose quality of differentiation is low, a region of cells that are undergoing differentiation (a cell adhesion region to be described below), and a region containing a region of impurities and the like in the cell culture process as an adhesion region. In other words, the adhesion region is a region containing cells in culture adhering to the culture vessel  19  or the substrate of the culture vessel  19  and impurities in the cell culture process. Also, the adhesion region area calculation unit  172  calculates an area of the adhesion region extracted by the adhesion region extraction unit  170 . 
     &lt;Regarding Cell Adhesion Region&gt; 
     The cell adhesion region extraction unit  180  extracts a cell adhesion region from the image P. As described above, in addition to the cells having the paving stone-shaped form described above, cells whose quality of differentiation is low or cells that are undergoing differentiation may adhere to the culture vessel. Cells whose quality of differentiation is low or cells that are undergoing differentiation do not form a paving stone-shaped form and are, for example, densely packed. The cell adhesion region extraction unit  180  extracts a region within the image P or a region of cells whose quality of differentiation is low or a region of cells that are undergoing differentiation among cell regions shown in the image P as a cell adhesion region. Here, the cell adhesion region is a region of cells adhering to the culture vessel other than the paving stone-shaped region (a region of cells having a paving stone-shaped form). Although the case where a factor that causes the cell adhesion region is that cells whose quality of differentiation is low or cells that are undergoing differentiation are densely packed without forming a paving stone-shaped form has been described above as an example, the factor that causes the cell adhesion region is not limited thereto. The cell adhesion region area calculation unit  182  calculates an area of the cell adhesion region extracted by the cell adhesion region extraction unit  180 . 
     &lt;Regarding Non-Cell Region&gt; 
     The non-cell region extraction unit  190  extracts a non-cell region from the image P. Here, the image P also shows a region where no cell is present. The region where no cell is present is, for example, a region where there are no cells in the culture medium or a region where there are no cells within the bottom surface (surface) of the culture vessel  19 . The non-cell region extraction unit  190  extracts a region where there are no cells as a non-cell region. The non-cell region area calculation unit  192  calculates an area of the non-cell region extracted by the non-cell region extraction unit  190 . 
     &lt;Regarding Dead Cell Region&gt; 
     The dead cell region extraction unit  200  extracts a dead cell region from the image P. Here, the cell may become a dead cell because the differentiation is not performed in the differentiation process. The dead cell region extraction unit  200  extracts a region of dead cells among the cells shown in the image P as the dead cell region. The dead cell region area calculation unit  202  calculates an area of the dead cell region extracted by the dead cell region extraction unit  200 . Also, the dead cells are, for example, cells in which the appearance of caspase 3, caspase 5, or the like is detected as fluorescence by staining with an existing fluorescent dye and fluorescence is detected by a 4′,6-diamidino-2-phenylindole (DAPI) stain. 
     &lt;Regarding Dying and Dead Cell Region&gt; 
     The dying and dead cell region extraction unit  210  extracts a dying and dead cell region from the image P. As described above, cells may become dead cells due to low-quality differentiation during a differentiation process. In the following description, cells that are likely to become dead cells with the elapse of time or cells that do not become dead cells and continue to differentiate in a state of live cells by subsequently giving an appropriate environment, an appropriate culture medium, or appropriate additives even if they will be likely to become dead cells are collectively referred to as dying cells. Also, the dying cells are, for example, cells in which the appearance of caspase 3, caspase 5, or the like is detected as fluorescence by staining with an existing fluorescent dye and which is not stained with 4′,6-diamidino-2-phenylindole (DAPI) (in which fluorescence based on DAPI is not detected). The dying and dead cell region extraction unit  210  extracts a region where a dying cell region and a dead cell region are combined as a dying and dead cell region. The dying and dead cell region area calculation unit  212  calculates an area of the dying and dead cell region extracted by the dying and dead cell region extraction unit  210 . 
     &lt;Regarding Image Processing on Image P&gt; 
       FIG.  18    is a flowchart showing an example of a process of the image determination device  10  of the third embodiment. The flowchart shown in  FIG.  18    is executed, for example, when the image P is acquired by the acquisition unit  110 . The paving stone region extraction unit  160  and the paving stone region-specific average luminance value calculation unit  162  extract a paving stone region according to the processing of steps S 720  to S 730  on a section SCT1 and calculate an average value of luminance values in the paving stone region. The cell adhesion region extraction unit  180  and the cell adhesion region area calculation unit  182  extract a cell adhesion region according to the processing of steps S 700  to S 712  on a section SCT2 and calculate an area of the cell adhesion region. 
     The non-cell region extraction unit  190  and the non-cell region area calculation unit  192  extract a non-cell region according to the processing of steps S 740  to S 758  on a section SCT3 and calculate an area of the non-cell region. The adhesion region extraction unit  170  and the adhesion region area calculation unit  172  extract an adhesion region according to the processing of steps S 760  to S 764  on a section SCT4 and calculate an area of the adhesion region. The dead cell region extraction unit  200  and the dead cell region area calculation unit  202  extract a dead cell region according to the processing of steps S 770  to S 774  on a section SCT5 and calculate an area of the dead cell region. The dying and dead cell region extraction unit  210  and the dying and dead cell region area calculation unit  212  extract a dying and dead cell region according to the processing of steps S 780  to S 784  on a section SCT6 and calculate an area of the dying and dead cell region. 
     In the present embodiment, a case where the image determination device  10  calculates any one of the average value of the luminance values in the paving stone region, the area of the adhesion region, the area of the cell adhesion region, the area of the non-cell region, the area of the dead cell region, and the area of the dying and dead cell region will be described. That is, the image determination device  10  executes the processing of any one of steps S 700  to S 784  shown in  FIG.  18   . Also, when the image determination device  10  calculates any one of the average value of the luminance values in the paving stone region, the area of the adhesion region, the area of the cell adhesion region, the area of the non-cell region, the area of the dead cell region, and the area of the dying and dead cell region, a configuration in which the section SCT according to a calculation target among the sections SCT1 to SCT6 is executed may be adopted. 
     &lt;Regarding Brightness of Each Part Shown in Image P&gt; 
     Hereinafter, the light and shade of each pixel constituting the image P will be described before description of details of the processing on each section SCT.  FIG.  19    is a diagram showing an overview of light and shade of each pixel constituting the image P. As described above, the image P imaged by the imaging device  34  provided in the culture observation device  11  of the present embodiment is a phase difference image in which a phase shift of light is detected as a contrast. A darkest portion imaged in this image P is the cytoplasm of the cells that adhere to the culture vessel. Also, the cytoplasm of cells (i.e., the cytoplasm of cells in the paving stone region) whose degree of adhesion to the culture vessel (hereinafter referred to as a degree of adhesion) is high among the cells that adhere to the culture vessel is imaged to be darker than the cytoplasm of cells (i.e., the cytoplasm of cells in the cell adhesion region) having a low degree of adhesion. As shown in  FIG.  19   , the luminance of the pixel showing the cytoplasm of a cell having a high degree of adhesion is luminance less than a threshold value TH1 and the luminance of the pixel showing the cytoplasm of a cell having a low degree of adhesion is luminance greater than or equal to the threshold value TH1. 
     Next to the cytoplasm, a darkest portion that is imaged is the bottom surface of the culture vessel  19  or the culture medium. As shown in  FIG.  19   , the luminance of the pixel showing the cytoplasm of the cell (i.e., the cytoplasm of the cell in the cell adhesion region) having a low degree of adhesion is luminance less than a threshold value TH2 and the luminance of the pixel showing the bottom surface of the culture vessel  19  or the culture medium is luminance greater than or equal to the threshold value TH2. The threshold value TH1 and the threshold value  112  have a relationship of threshold value TH1&lt;threshold value TH2. Next to the bottom surface of the culture vessel  19  or the culture medium, a darkest portion that is imaged is the cell membrane of the cell. As shown in  FIG.  19   , the luminance of the pixel showing the bottom surface of the culture vessel  19  or the culture medium is luminance less than a threshold value TH3 and the luminance of the pixel showing the cell membrane of the cell is luminance greater than or equal to the threshold value TH3. The threshold value TH2 and the threshold value TH3 have a relationship of threshold value TH2&lt;threshold value TH3. 
     Next to the cell membrane of the cell, a darkest portion that is imaged is garbage generated when the culture medium is replaced, cell residue generated when the cell is crushed, or a dying cell. As shown in  FIG.  19   , the luminance of the pixel showing the cell membrane of the cell is luminance less than a threshold value TH4 and the luminance of the pixel showing garbage generated when the culture medium is replaced, cell residue generated when the cell is crushed, or a dying cell is luminance greater than or equal to the threshold value TH4. The threshold value TH3 and the threshold value TH4 have a relationship of threshold value TH3&lt;threshold value TH4. A brightest portion imaged in this image P is garbage generated when the culture medium is replaced, cell residue generated when the cell is crushed, or a dead cell. As shown in  FIG.  19   , the luminance of the pixel showing garbage generated when the culture medium is replaced, cell residue generated when the cell is crushed, or a dying cell is luminance less than the threshold value TH5 and the luminance of the pixel showing garbage generated when the culture medium is replaced, cell residue generated when the cell is crushed, or a dead cell is luminance greater than or equal to the threshold value TH5. The threshold value TH4 and the threshold value TH5 have a relationship of threshold value TH4&lt;threshold value TH5. The above description of light and shade is reversed according to whether a phase difference caused when diffracted light passing through the phase plate and direct light not passing through the phase plate interfere with each other when cells are imaged is a weakening phase difference (½ wavelength) or a strengthening phase difference (0 wavelengths). 
     &lt;Regarding Section SCT2: Image Processing on Cell Adhesion Region&gt; 
     Details of each section SCT will be described below with reference to  FIG.  18   . Because data acquired in the section SCT2 is used for processing on the section SCT1, the section SCT2 will first be described and then description will be given in the order of the section SCT1 and the sections SCT3 to 6. 
     In the section SCT2, the cell adhesion region extraction unit  180  performs a process of flattening the background of an image P with respect to the image P acquired by the acquisition unit  110  (step S 700 ) and applies the morphology filter (step S 702 ). Also, the process of flattening the background of the image P is, for example, the existing image processing for limiting the variation in a luminance value of the background of the image P. Here, the image P includes a region having high luminance caused by garbage generated when the culture medium is replaced, shot noise at the time of imaging by the imaging device  34 , or the like. In the section SCT2, the cell adhesion region extraction unit  180  performs a process of making an image of a structure finer than the cell adhesion region (for example, an image of a fine structure or the like within the cytoplasm such as an organelle) existing in the image P inconspicuous while correcting the luminance value of the image P so that a region having high luminance is inconspicuous according to steps S 700  and S 702 . 
     Subsequently, the cell adhesion region extraction unit  180  performs a threshold value-specific process on the image P and extracts a region having a luminance value less than or equal to the luminance value of the cell adhesion region (step S 704 ). As described above, the cell adhesion region is a region where cells whose quality of differentiation is low or cells that are undergoing differentiation are densely packed. Therefore, in step S 704 , the cell adhesion region extraction unit  180  extracts a region of the image P having a luminance value less than the threshold value TH2. Subsequently, the cell adhesion region extraction unit  180  applies the morphology filter to clarify the extracted region (step S 706 ). 
     Subsequently, the cell adhesion region extraction unit  180  performs a labeling process for the region clarified by the morphology filter (step S 708 ). The cell adhesion region extraction unit  180  can extract pixels labeled in the labeling process as a region and fill the missing pixels generated in the region with the extracted pixels. The missing pixels are, for example, a portion (for example, about several pixels) of the cell adhesion region having a luminance value greater than the threshold value TH2, and is a portion not extracted in step S 704 . Also, when a labeling value (i.e., an area of the region) of the region labeled in the labeling process does not match a value according to a size (an area) of the cell adhesion region (for example, excessively large), the cell adhesion region extraction unit  180  excludes the region from an extraction target. Here, in step S 704 , the extracted region of the luminance value less than the threshold value TH2 includes a cytoplasmic region of the cell having a paving stone-shaped form (see  FIG.  19   ). Generally, the cell adhesion region is a region smaller than the paving stone region. Therefore, the cell adhesion region extraction unit  180  excludes the paving stone region by excluding a region (for example, excessively large) that does not match the value according to the size (the area) of the cell adhesion region from the extracted region in the labeling process. Subsequently, the cell adhesion region extraction unit  180  applies the morphology filter to make the contour of the extracted region clearer (step S 710 ). The cell adhesion region extraction unit  180  can extract the cell adhesion region according to the processing of steps S 700  to S 710  described above. 
       FIG.  20    is a diagram showing an example of a cell adhesion region. The cell adhesion region extraction unit  180  can extract the cell adhesion region as shown in  FIG.  20    by performing the above-described processing of steps S 700  to S 710  on the image P. Returning to  FIG.  18   , the cell adhesion region area calculation unit  182  calculates an area of the cell adhesion region extracted by the cell adhesion region extraction unit  180  (step S 712 ). 
     &lt;Section SCT1: Image Processing on Paving Stone Region&gt; 
     In the section SCT1, the paving stone region extraction unit  160  applies the morphology filter to the image P acquired by the acquisition unit  110  (step S 720 ), performs a process of flattening the background of the image P (step S 722 ), and performs a process of smoothing the entire image P (step S 724 ). The paving stone region extraction unit  160  performs a process of making an image of a structure finer than a cell having the paving stone-shaped form (for example, an image of a fine structure or the like within the cytoplasm such as an organelle) existing in the image P inconspicuous while correcting the luminance value of the image P so that a region having high luminance caused by garbage generated when the culture medium is replaced, shot noise at the time of imaging by the imaging device  34 , or the like is inconspicuous to clarify a region of cells having a paving stone-shaped form according to the processing of steps S 720  to S 724 . 
     Subsequently, the paving stone region extraction unit  160  acquires a cell adhesion region extracted by the cell adhesion region extraction unit  180  performing the processing of steps S 700  to S 710  on the section SCT2 and extracts a region matching a region other than the cell adhesion region. In the region, a region having a luminance value greater than or equal to a prescribed threshold value is extracted (step S 726 ). Subsequently, the paving stone region extraction unit  160  determines whether or not cells have formed the paving stone region (step S 728 ). 
     Here, the paving stone region extraction unit  160  determines whether or not cells have formed the paving stone region on the basis of, for example, an average value obtained by averaging differences between luminance values in the cytoplasm of the cells imaged in the image P and luminance values in the cell membranes with respect to all cells imaged in the image P. The paving stone region extraction unit  160  determines that cells have formed the paving stone region when the average value is greater than or equal to a prescribed threshold value (for example, a threshold value obtained by subtracting a prescribed constant from the threshold value calculated as the paving stone region-specific average luminance threshold value or the paving stone region-specific average luminance threshold value in the region within the image P). On the other hand, when the average value is less than a prescribed threshold value, the paving stone region extraction unit  160  determines that cells have not formed the paving stone region. Also, the paving stone region extraction unit  160  may determine that cells have formed the paving stone region, for example, when an area of the paving stone region extracted from the image P is larger than a prescribed value. 
     Subsequently, the paving stone region-specific average luminance value calculation unit  162  calculates an average value of luminance values of the pixels showing the paving stone region extracted by the paving stone region extraction unit  160  (luminance values of pixels showing a region of cells having a paving stone-shaped form) (step S 730 ). 
       FIG.  21    is a diagram showing an example of a paving stone region (a region surrounded by an alternate long and short dash line) extracted by the paving stone region extraction unit  160  through step S 726 . The paving stone region extraction unit  160  can reduce a luminance value difference between the cell membrane and the cytoplasm between which the luminance value difference is larger in the cells having the paving stone-shaped form on the image P by performing the above-described processing of steps S 720  to S 726  on the image P and facilitate a threshold value comparison process. Also, in the example shown in  FIG.  21   , a portion having a high-luminance value is indicated by a light color and a portion having a low-luminance value is indicated by a dark color. 
     &lt;Section SCT3: Image Processing on Non-Cell Region&gt; 
     In the section SCT3, the non-cell region extraction unit  190  applies the morphology filter to the image P acquired by the acquisition unit  110  (step S 740 ) and performs a process of flattening the background of the image P (step S 742 ). The non-cell region extraction unit  190  performs a process of making an image of a structure finer than the paving stone region and the cell adhesion region (for example, an image of a fine structure or the like within the cytoplasm such as an organelle) existing in the image P inconspicuous while correcting the luminance value of the image P so that a region having high luminance caused by garbage generated when the culture medium is replaced, shot noise at the time of imaging by the imaging device  34 , or the like is inconspicuous according to the processing of steps S 740  and S 742 . Subsequently, the non-cell region extraction unit  190  performs a threshold value-specific process on the image P and extracts a region having luminance less than or equal to that of the cytoplasm of the cell having a low degree of adhesion (step S 744 ). Specifically, the non-cell region extraction unit  190  extracts a region of pixels having a luminance value less than the threshold value TH2 (i.e., the cytoplasm of the paving stone region and the cytoplasm of the cell adhesion region) in the image P. 
       FIG.  22    is a diagram showing an example of a region to be extracted in step S 744 . As shown in a compartment pt of  FIG.  22   , the luminance values in the cytoplasm of the paving stone region and the cytoplasm of the cell adhesion region among the cytoplasm of the paving stone region, the cytoplasm of the cell adhesion region, and the non-cell region are less than the luminance value in the non-cell region. In step S 744 , regions of the cytoplasm of the paving stone region and the cytoplasm of the cell adhesion region are extracted. The regions extracted in step S 744  are used in the processing of step S 756  to be described below. 
     Subsequently, the non-cell region extraction unit  190  applies the morphology filter to clarify a portion where a local change is large in the image P (step S 746 ).  FIG.  23    is a diagram conceptually showing local changes. In  FIG.  23   , two prescribed ranges (prescribed ranges AR1 and AR2 shown in  FIG.  23   ) among prescribed ranges (hereinafter, prescribed ranges AR) are shown in the image P. In  FIG.  23   , the prescribed range AR1 is set in, for example, a non-cell region or a cell region having relatively large cytoplasm in the image P and the prescribed range AR2 is set in a region (for example, a paving stone region, a cell adhesion region, an adhesion region, or the like) where there are cells in the image P. As shown in  FIG.  23   , in general, a change in the luminance value between adjacent pixels is larger (i.e., a local change is larger) with respect to pixels within the prescribed range AR2 among pixels within the prescribed range AR1 which is a non-cell region or a cell region having relatively large cytoplasm and pixels within the prescribed range AR2 of a region where there are cells of a paving stone-shaped form or the like. In step S 746 , the non-cell region extraction unit  190  clarifies the portion where the local change is large (i.e., the region where there are cells) in the image P. 
     Subsequently, the non-cell region extraction unit  190  performs a subtraction process of subtracting a region where a portion having a large local change is clarified in step S 746  from the image P acquired by the acquisition unit  110  (step S 748 ). Specifically, the non-cell region extraction unit  190  acquires a portion obtained by subtracting the portion having the large local change from the image P. Thereby, the non-cell region extraction unit  190  can acquire a region having a small local change (i.e., a non-cell region or a region of a cell having large cytoplasm). 
     Subsequently, the non-cell region extraction unit  190  performs a threshold value-specific process on a region having a small local change acquired according to the processing of steps S 746  and S 748  and extracts a region having a luminance value less than or equal to the luminance value of the non-cell region (step S 750 ). Here, a region having a small local change may also not be a non-cell region (for example, a region of a cell having large cytoplasm or the like). In step S 750 , the non-cell region extraction unit  190  can exclude a region (for example, a cell region with large cytoplasm or the like) that is not the non-cell region by extracting a region having a luminance value less than the threshold value TH3 from among regions having small local changes acquired according to the processing of steps S 746  and S 748 . 
     Subsequently, the non-cell region extraction unit  190  performs a labeling process for the regions extracted according to the processing of steps S 746  to S 750  (step S 752 ). By performing the labeling process, the missing pixels occurring in the extracted region are filled in. These missing pixels are, for example, a portion having a luminance value greater than the threshold value TH3 only for a part (for example, about several pixels) within a region having a small local change. Subsequently, the non-cell region extraction unit  190  applies the morphology filter and makes the contour of the region filled with the missing pixels clearer (step S 754 ). The cell adhesion region extraction unit  180  can extract a region having a small local change and a luminance value less than the threshold value TH3 according to the processing of steps S 746  to S 754  described above. 
     Subsequently, the non-cell region extraction unit  190  performs a subtraction process of subtracting a region having a luminance value less than the threshold value TH2 extracted according to the processing of steps S 740  to S 744  from a region having a luminance value less than the threshold value TH3 extracted according to the processing of steps S 746  to S 754  described above (step S 756 ). As shown in  FIG.  19   , the bottom surface of the culture vessel  19  or the region of the culture medium (i.e., the non-cell region) can be extracted by subtracting the region having the luminance value less than the threshold value TH2 from the region having the luminance value less than the threshold value TH3. 
       FIG.  24    is a diagram showing an example of a non-cell region. In  FIG.  24   , a non-cell region (a darkly colored region shown in  FIG.  24   ) is shown in the image P. Also, a compartment pt shown in  FIG.  24    shows the same position as the compartment pt shown in  FIG.  22   . As shown in the compartment pt of  FIG.  24   , the non-cell region does not include the cytoplasmic region of the cell because the cytoplasmic region is subtracted in step S 756 . Returning to  FIG.  18   , the non-cell region area calculation unit  192  calculates an area of the non-cell region extracted by the non-cell region extraction unit  190  (step S 758 ). 
     &lt;Section SCT4: Image Processing on Adhesion Region&gt; 
     In the section SCT4, the adhesion region extraction unit  170  performs a threshold value-specific process of extracting a region having a luminance value less than the threshold value TH3 from the image P acquired by the acquisition unit  110  (step S 760 ). As described above, the region having the luminance value less than the threshold value TH3 includes an adhesion region and a non-cell region (see  FIG.  19   ). Subsequently, the adhesion region extraction unit  170  acquires the non-cell region extracted by the non-cell region extraction unit  190  according to the processing of steps S 740  to S 756  and performs a subtraction process of subtracting the non-cell region from the region having the luminance value less than the threshold value TH3 (step S 762 ). 
       FIG.  25    is a diagram showing an example of an adhesion region. In  FIG.  25   , the adhesion region (an adhesion region AR3 shown in  FIG.  25   ) is shown in the image P. Returning to  FIG.  18   , the adhesion region area calculation unit  172  subsequently calculates an area of the adhesion region extracted by the adhesion region extraction unit  170  (step S 764 ). 
     &lt;Section SCT5: Image Processing on Dead Cell Region&gt; 
     In the section SCT5, the dead cell region extraction unit  200  applies the morphology filter to the image P acquired by the acquisition unit  110  (step S 770 ) and performs a process of flattening the background of the image P (step S 772 ). The dead cell region extraction unit  200  performs a process of making an image of a structure finer than a dead cell (for example, an image of a fine structure or the like within the cytoplasm such as an organelle) existing in the image P inconspicuous while correcting the luminance value of the image P so that a region having high luminance caused by garbage generated when the culture medium is replaced, shot noise at the time of imaging by the imaging device  34 , or the like is inconspicuous to clarify a dead cell region according to the processing of steps S 770  and S 772 . 
     Subsequently, the dead cell region extraction unit  200  performs a threshold value-specific process for the image P and extracts a region having luminance higher than or equal to that of the dead cell region (step S 773 ). Specifically, the dead cell region extraction unit  200  extracts a region of pixels having a luminance value greater than or equal to the threshold value TH5 in the image P.  FIG.  26    is a diagram showing an example of a dead cell region. In  FIG.  26   , the dead cell region (a dead cell region AR4 shown in  FIG.  26   ) is shown in the image P. Returning to  FIG.  18   , the dead cell region area calculation unit  202  subsequently calculates an area of the dead cell region extracted by the dead cell region extraction unit  200  (step S 774 ). 
     &lt;Section SCT6: Image Processing on Dying and Dead Cell Region&gt; 
     In the section SCT6, the dying and dead cell region extraction unit  210  applies the morphology filter to the image P acquired by the acquisition unit  110  (step S 780 ) and performs a process of flattening the background of the image P (step S 782 ). The dying and dead cell region extraction unit  210  performs a process of making an image of a structure finer than a dying and dead cell region (for example, an image of a fine structure or the like within the cytoplasm such as an organelle) existing in the image P inconspicuous while correcting the luminance value of the image P so that a region having high luminance caused by garbage generated when the culture medium is replaced, shot noise at the time of imaging by the imaging device  34 , or the like is inconspicuous to clarify the dying and dead cell region according to the processing of steps S 780  and S 782 . Thereby, the dying and dead cell region extraction unit  210  can clarify the dying and dead cell region. 
     Subsequently, the dying and dead cell region extraction unit  210  performs a threshold value-specific process for the image P and extracts a region having luminance higher than or equal to that of the dying and dead cell region (step S 783 ). Specifically, the dying and dead cell region extraction unit  210  extracts a region of a pixel having a luminance value greater than or equal to the threshold value TH4 in the image P.  FIG.  27    is a diagram showing an example of a dying and dead cell region. In  FIG.  27   , a dying and dead cell region (a dying and dead cell region AR5 shown in  FIG.  27   ) is shown in the image P. Returning to  FIG.  18   , the dying and dead cell region area calculation unit  212  subsequently calculates an area of the dying and dead cell region extracted by the dying and dead cell region extraction unit  210  (step S 784 ). 
     Also, the extraction process for each region in the above-described sections SCT1 to SCT6 is an example and the present invention is not limited thereto. The combination and order of region extraction processes may be changed as appropriate, all or some extraction processes may be replaced with other existing image processing, or the other existing image processing may be added to the extraction processes. 
     &lt;Regarding Determination Unit  220 : Determination Based on Average Value of Luminance Values in Paving Stone Region&gt; 
     The determination unit  220  determines the quality of cell differentiation on the basis of the average value of the luminance values in the paving stone region calculated by the paving stone region-specific average luminance value calculation unit  162  and the paving stone region-specific average luminance threshold value  301   a  stored in the storage unit  301 . Here, the paving stone region-specific average luminance threshold value  301   a  is a threshold value for the average value of the luminance values in the paving stone region. The determination unit  220  determines the quality of cell differentiation by comparing the average value of the luminance values in the paving stone region with the paving stone region-specific average luminance threshold value  301   a . For example, the determination unit  220  determines that the quality of cell differentiation is high when the acquired average luminance value of the paving stone region is greater than or equal to the average value of the luminance values in the paving stone region indicated by the paving stone region-specific average luminance threshold value  301   a  and determines that the quality of cell differentiation is low when the acquired average luminance value of the paving stone region is less than the average value of the luminance values in the paving stone region indicated by the paving stone region-specific average luminance threshold value  301   a.    
     Here, as described above, the paving stone region-specific average luminance threshold value  301   a  is a value predetermined by comparing an index based on an image P in which the cell observer observes cells and a cell determined to be a normal mature hepatocyte is imaged with an index based on an image P in which the cell observer observes cells and a cell determined not to be a normal mature hepatocyte is imaged. That is, the paving stone region-specific average luminance threshold value  301   a  is a value based on a result of determining the quality of differentiation in a time-lapse image. Therefore, the determination unit  220  determines the quality of cell differentiation by comparing an index value based on the time-lapse image (the average value of the luminance values in the paving stone region) with an index value based on the result of determining the quality of differentiation in the time-lapse image (the paving stone region-specific average luminance threshold value  301   a ). 
     &lt;Regarding Determination Unit  220 : Determination Based on Adhesion Region&gt; 
     Also, the determination unit  220  determines the quality of cell differentiation on the basis of the area of the adhesion region calculated by the adhesion region area calculation unit  172  and the adhesion region area-specific threshold value  301   b  stored in the storage unit  301 . Here, the adhesion region area-specific threshold value  301   b  is a threshold value for the area of the adhesion region. For example, the determination unit  220  determines that the quality of cell differentiation is high when the value of the acquired area of the adhesion region is a value greater than or equal to the threshold value for the area of the adhesion region indicated by the adhesion region area-specific threshold value  301   b  and determines that the quality of cell differentiation is low when the value of the acquired area of the adhesion region is a value less than the threshold value for the area of the adhesion region indicated by the adhesion region area-specific threshold value  301   b.    
     &lt;Regarding Determination Unit  220 : Determination Based on Cell Adhesion Region&gt; 
     Also, the determination unit  220  determines the quality of cell differentiation on the basis of the area of the cell adhesion region calculated by the cell adhesion region area calculation unit  182  and the cell adhesion region area-specific threshold value  301   c  stored in the storage unit  301 . Here, the cell adhesion region area-specific threshold value  301   c  is a threshold value for the area of the cell adhesion region. For example, the determination unit  220  determines that the quality of cell differentiation is low when the value of the acquired area of the cell adhesion region is a value greater than or equal to the threshold value for the area of the cell adhesion region indicated by the cell adhesion region area-specific threshold value  301   c  and determines that the quality of cell differentiation is high when the value of the acquired area of the cell adhesion region is a value less than the threshold value for the area of the cell adhesion region indicated by the cell adhesion region area-specific threshold value  301   c.    
     &lt;Determination Unit  220 : Determination Based on Non-Cell Region&gt; 
     Also, the determination unit  220  determines the quality of cell differentiation on the basis of an area of the non-cell region calculated by the non-cell region area calculation unit  192  and the non-cell region area-specific threshold value  301   d  stored in the storage unit  301 . Here, the non-cell region area-specific threshold value  301   d  is a threshold value for the area of the non-cell region. For example, the determination unit  220  determines that the quality of cell differentiation is low when the value of the acquired area of the non-cell region is a value greater than or equal to the threshold value for the area of the non-cell region indicated by the non-cell region area-specific threshold value  301   d  and determines that the quality of cell differentiation is high when the value of the acquired area of the non-cell region is a value less than the threshold value for the area of the non-cell region indicated by the non-cell region area-specific threshold value  301   d.    
     &lt;Determination Unit  220 : Determination Based on Dead Cell Region&gt; 
     Also, the determination unit  220  determines the quality of cell differentiation on the basis of the area of the dead cell region calculated by the dead cell region area calculation unit  202  and the dead cell region area-specific threshold value  301   e  stored in the storage unit  301 . Here, the dead cell region area-specific threshold value  301   e  is a threshold value for the area of the dead cell region. For example, the determination unit  220  determines that the quality of cell differentiation is low when the value of the acquired area of the dead cell region is a value greater than or equal to the threshold value for the area of the dead cell region indicated by the dead cell region area-specific threshold value  301   e  and determines that the quality of cell differentiation is high when the value of the acquired area of the dead cell region is a value less than the threshold value for the area of the dead cell region indicated by the dead cell region area-specific threshold value  301   e.    
     &lt;Determination Unit  220 : Determination Based on Dying and Dead Cell Regions&gt; 
     Also, the determination unit  220  determines the quality of cell differentiation on the basis of the area of the dying and dead cell region calculated by the dying and dead cell region area calculation unit  212  and the dying and dead cell region area-specific threshold value  301   f  stored in the storage unit  301 . Here, the dying and dead cell region area-specific threshold value  301   f  is a threshold value for the area of the dying and dead cell region. For example, the determination unit  220  determines that the quality of the cell differentiation is low when the value of the acquired area of the dying and dead cell region is a value greater than or equal to the threshold value for the area of the dying and dead cell region indicated by the dying and dead cell region area-specific threshold value  301   f  and determines that the quality of the cell differentiation is high when the value of the acquired area of the dying and dead cell region is a value less than the threshold value for the area of the dying and dead cell region indicated by the dying and dead cell region area-specific threshold value  301   f.    
     &lt;Regarding Output Unit  230 &gt; 
     The output unit  230  outputs a determination result in a determination process of the determination unit  220 . The output unit  230  is, for example, a display device including a liquid crystal display panel or an organic electroluminescence (EL) display panel. Also, the output unit  230  is connected to, for example, a collection device for collecting the determination result via a network, and outputs the determination result in the determination process of the determination unit  220  to the collection device. This collection device is, for example, a terminal device used by a referrer who refers to a result of determining the quality of cell differentiation, and may be implemented by a portable communication terminal device such as a smartphone or a tablet-type computer (a tablet PC) or may be implemented by a stationary personal computer or the like. 
     Also, when the magnification of a lens of the imaging device  34  and the resolution of the image P are predetermined, the adhesion region area calculation unit  172 , the cell adhesion region area calculation unit  182 , the non-cell region area calculation unit  192 , the dead cell region area calculation unit  202 , and the dying and dead cell region area calculation unit  212  may be configured to supply the number of pixels indicating the extracted region as an area to the determination unit  220 . In this case, the adhesion region area-specific threshold value  301   b , the cell adhesion region area-specific threshold value  301   c , the non-cell region area-specific threshold value  301   d , the dead cell region area-specific threshold value  301   e , and the dying and dead cell region area-specific threshold value  301   f  are threshold values for the number of pixels and the determination unit  220  determines the quality of cell differentiation on the basis of the number of pixels calculated in each functional unit and the threshold value for the number of pixels stored in the storage unit  301 . 
     &lt;Regarding Effect Quantity r&gt; 
     Here, it may be preferable to use an image P of cells imaged by the imaging device  34  at a specific time in a process in which each functional unit extracts each region and a process of calculating each value. Hereinafter, a process in which the effect quantity calculation unit  219  calculates an effect quantity r in the paving stone region and identifies an image P suitable for use in an evaluation process of the determination unit  220  will be described as an example. Because a process of calculating the effect quantity r in the other regions in the effect quantity calculation unit  219  is basically similar, description thereof will be omitted. 
     As described above, the imaging device  34  generates an image P according to time-lapse observation in a series of culture processes (for 21 days of the first to fourth differentiation-inducing processes). In the storage unit  301 , for example, the time when the image P was acquired by the imaging device  34  and the average value of the luminance values in the paving stone region calculated by the paving stone region-specific average luminance value calculation unit  162  using the image P acquired at the time are stored in association with each other. The observer determines the quality of cell differentiation by observing the final image P (for example, an image after 21 days from the start of the culture process when the culture process ends in the 21 days) among a plurality of images P stored in the storage unit  301 . Hereinafter, it is assumed that the determination results of the observer are all associated with the plurality of images P related to the series of culture processes stored in the storage unit  301 . 
     Here, the plurality of images P are time-lapse images each associated with the time when the image P is acquired by the imaging device  34  (i.e., the elapsed time from the start of the culture process). Also, the average value of the luminance values in the paving stone region is an index value calculated on the basis of the image P. Therefore, the index value for determining the quality of differentiation in the time-lapse image is calculated from the time-lapse image in which the elapsed time from the start of the culture process and the result of determining the quality of differentiation are associated. 
     Also, the observer may not observe the final image P and determine the quality of cells. Although it is desirable to make the determination using the image P acquired near the time when the culture process ends, the determination may be made using the image P acquired at any time after the start of the culture process. Also, the observer may not determine the quality of cell differentiation by observing the cells. The observer may make the determination using, for example, an existing biochemical index. In this case, for example, the determination may be made in an ELISA method. 
     The effect quantity r is an index indicating the accuracy (effect) of a certain index (for example, an evaluation index such as an average value of the luminance values in the paving stone region in the present embodiment) with respect to a process of determining a certain phenomenon (the quality of differentiation into cells in the present embodiment). When the effect quantity r is calculated, the effect quantity calculation unit  219  calculates the effect quantity r using Eq. (1) when the number of images P determined by the observer to have high-quality cell differentiation among the images P stored in the storage unit  301  is set as the number of successful groups n1 and the number of images P determined by the observer to have low-quality cell differentiation is set as the number of unsuccessful groups n2. For example, the number of successful groups n1 is the number of images P for which a ratio of the number of normal mature hepatocytes to the final number of cells is determined to be higher than or equal to a standard ratio (for example, 80%) and the number of unsuccessful groups n2 is the number of images P for which the ratio of the number of normal mature hepatocytes to the final number of cells is determined to be lower than the standard ratio. A method of sorting the successful group and the unsuccessful group is not limited to the ratio between the numbers and the existing sorting method can be applied. For example, a sorting process may be performed on the basis of the shape of the final cell (for example, on the basis of a shape bias). 
     
       
         
           
             
               
                 
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     A test statistic z is a value based on the standard normal distribution and is a value acquired on the basis of a significance probability (a p value). Therefore, the accuracy of a process of determining a phenomenon increases as the value of the effect quantity r increases. Therefore, preferably, the image P at the time when the value of the effect quantity r is large is used for the determination process of the determination unit  220 . 
       FIG.  28    is a diagram showing an example of the effect quantity (hereinafter referred to as the effect quantity r) of the average value of the luminance values in the paving stone region. In  FIG.  28   , a change over time in the effect quantity r of the average value of the luminance values in the paving stone region based on images P obtained by imaging two types of iPS cells (TkDA and 1383D6) of different cell lines cultured in the culture medium is shown. As shown in  FIG.  28   , the time when the effect quantity r of the average value of the luminance values in the paving stone region becomes relatively greater than in other time periods is about 138 to 144 hours from the start of a cell culture process in the case of the iPS cells of the TkDA cell line and the time when the effect quantity r of the average value of the luminance values in the paving stone region becomes relatively greater than in other time periods is about 132 hours from the start of a cell culture process in the case of iPS cells of the 1383D6 cell line. Therefore, it is preferable that the image P used when the paving stone region-specific average luminance value calculation unit  162  for the luminance value of the paving stone region calculates the average value of the luminance values in the paving stone region be an image P captured during 138 to 144 hours from the start of the culture process in the case of the iPS cells of the TkDA cell line and it is preferable that the image P used when the paving stone region-specific average luminance value calculation unit  162  for the luminance values in the paving stone region calculates the average value of the luminance values in the paving stone region be an image P captured after 132 hours from the start of the culture process in the case of the iPS cells of the 1383D6 cell line. Also, the paving stone region-specific average luminance value calculation unit  162  may obtain the average value of the luminance values in the paving stone region in the image P of the cells of any one of cell lines including the TkDA cell line and the 1383D6 cell line captured at at least one point in time after about 132 hours or 138 to 144 hours from the start of the cell culture process. Also, the time such as 132 hours or 138 to 144 hours is an example and the present invention is not limited thereto. Any time period is preferred as long as it is a time period when the effect quantity r of the average value of the luminance values in the paving stone region in the image P used when the average value of the luminance values in the paving stone region is 0.5 or more. Here, it is generally considered that the reliability of a target index for which the effect quantity r is 0.5 or more is high. Thus, it is preferable to use the image P of the time period when the effect quantity r is 0.5 or more. For example, it is also preferable that the time period be a time period when the effect quantity r of the average value of the luminance values in the paving stone region in the image P of the cells of at least one of the TkDA cell line and the 1383D6 cell line is 0.5 or more. Also, the cells are not limited to the TkDA cell line or the 1383D6 cell line and cells of other cell lines may be determined by the determination unit  220 . For example, the paving stone region-specific average luminance value calculation unit  162  may calculate an average value of the luminance values in the paving stone region from images of cells of other cell lines captured in a time period when the effect quantity r of the average value of the luminance values in the paving stone region in the image P of the cells of at least one of the TkDA cell line and the 1383D6 cell line is 0.5 or more. Also, the time period when effect quantities r of the average values of the luminance values in the paving stone regions in the images P of cells of both the TkDA cell line and the 1383D6 cell line are 0.5 or more is, for example, a time period after 57 hours from the start of the culture process for the iPS cells as an example. Alternatively, it may be a time period after 57 hours from the start of differentiation. Also, the determination unit  220  may not make the determination for the luminance values in the paving stone region in the image P captured in the time period when the effect quantity r is 0.5 or more on the basis of the effect quantity r or may make the determination on the basis of effect quantities r of other values. 
       FIG.  29    is a diagram showing an example of the effect quantity of the area of the cell adhesion region. In  FIG.  29   , a change over time in the effect quantity r of the area of the cell adhesion region based on an image P obtained by imaging two types of iPS cells (TkDA and 1383D6) of different cell lines cultured in the culture medium is shown. As shown in  FIG.  29   , the time when the effect quantity r of the area of the cell adhesion region becomes relatively greater than in other time periods is about 51 hours from the start of a cell culture process in the case of the iPS cells of the TkDA cell line and the time when the effect quantity r of the area of the cell adhesion region becomes relatively greater than in other time periods is about 48 hours from the start of a cell culture process in the case of iPS cells of the 1383D6 cell line. It is preferable that the image P used when the cell adhesion region extraction unit  180  extracts the cell adhesion region be an image P captured during 48 hours from the start of the culture process in the case of the iPS cells of the TkDA cell line and it is preferable that the image P used when the cell adhesion region extraction unit  180  extracts the cell adhesion region be an image P captured after 51 hours from the start of the culture process in the case of the iPS cells of the 1383D6 cell line. Also, the cell adhesion region area calculation unit  182  may obtain the area of the cell adhesion region in the image P of the cells of any one of cell lines including the TkDA cell line and the 1383D6 cell line captured at at least one point in time after about 51 hours and 48 hours from the start of the cell culture process. Also, the time such as 51 hours or 48 hours is an example and the present invention is not limited thereto. The image P used when the area of the cell adhesion region is calculated by the cell adhesion region area calculation unit  182  may be preferably an image captured in any time period as long as it is a time period when the effect quantity r of the area of the cell adhesion region is 0.5 or more. For example, it is also preferable that the time period be a time period when the effect quantity r of the area of the cell adhesion region in the image P of the cells of at least one of the TkDA cell line and the 1383D6 cell line is 0.5 or more. Also, the cell is not limited to the TkDA cell line or the 1383D6 cell line and cells of other cell lines may be determined by the determination unit  220 . For example, the cell adhesion region area calculation unit  182  may calculate the area of the cell adhesion region from images of cells of other cell lines captured in a time period when the effect quantity r of the area of the cell adhesion region in the image P of the cells of at least one of the TkDA cell line and the 1383D6 cell line is 0.5 or more. Also, the time period when effect quantities r of the area of the cell adhesion region in the images P of cells of both the TkDA cell line and the 1383D6 cell line are 0.5 or more is, for example, a time period after 51 to 60 hours from the start of the culture process as an example. Also, the determination unit  220  may not make the determination for the area of the cell adhesion region in the image P captured in the time period when the effect quantity r is 0.5 or more on the basis of the effect quantity r or may make the determination on the basis of effect quantities r of other values. 
       FIG.  30    is a diagram showing an example of the effect quantity of the area of the adhesion region. In  FIG.  30   , a change over time in the effect quantity r of the area of the adhesion region based on an image P obtained by imaging two types of iPS cells (TkDA and 1383D6) cultured in the culture medium is shown. As shown in  FIG.  30   , the time when the effect quantity r of the area of the adhesion region becomes relatively greater than in other time periods is after about 141 hours from the start of a cell culture process in the case of the iPS cells of the TkDA cell line and the time when the effect quantity r of the area of the adhesion region becomes relatively greater than in other time periods is after about 3% hours from the start of a cell culture process in the case of iPS cells of the 1383D6 cell line. Therefore, it is preferable that the image P used when the adhesion region extraction unit  170  extracts the adhesion region be an image P captured after 141 hours from the start of the culture process in the case of the iPS cells of the TkDA cell line and it is preferable that the image P used when the adhesion region extraction unit  170  extracts the adhesion region be an image P captured after 3% hours from the start of the culture process in the case of the iPS cells of the 1383D6 cell line. Also, the adhesion region area calculation unit  172  may obtain the area of the adhesion region in the image P of the cells of any one of cell lines including the TkDA cell line and the 1383D6 cell line captured at at least one point in time after about 141 hours and 396 hours from the start of the cell culture process. Also, the time such as 141 hours or 396 hours is an example and the present invention is not limited thereto. The image P used when the area of the adhesion region is calculated by the adhesion region area calculation unit  172  may be preferably an image captured in any time period as long as it is a time period when the effect quantity r of the area of the adhesion region is 0.5 or more. For example, it is also preferable that the time period be a time period when the effect quantity r of the area of the adhesion region in the image P of the cells of at least one of the TkDA cell line and the 1383D6 cell line is 0.5 or more. Also, the cell is not limited to the TkDA cell line or the 1383D6 cell line and cells of other cell lines may be determined by the determination unit  220 . For example, the adhesion region area calculation unit  172  may calculate the area of the adhesion region from images of cells of other cell lines captured in a time period when the effect quantity r of the area of the adhesion region in the image P of the cells of at least one of the TkDA cell line and the 1383D6 cell line is 0.5 or more. Also, the time period when effect quantities r of the area of the adhesion region in the images P of cells of both the TkDA cell line and the 1383D6 cell line are 0.5 or more is, for example, a time period after 240 hours or 360 to 405 hours from the start of the culture process as an example. Also, the determination unit  220  may not make the determination for the area of the adhesion region in the image P captured in the time period when the effect quantity r is 0.5 or more on the basis of the effect quantity r or may make the determination on the basis of effect quantities r of other values. 
       FIG.  31    is a diagram showing an example of the effect quantity of the area of the dead cell region. In  FIG.  31   , a change over time in the effect quantity r of the area of the dead cell region based on an image P obtained by imaging two types of iPS cells (TkDA and 1383D6) cultured in the culture medium is shown. As shown in  FIG.  31   , the time when the effect quantity r of the area of the dead cell region becomes relatively greater than in other time periods is after about 486 hours from the start of a cell culture process in the case of the iPS cells of the TkDA cell line and the time when the effect quantity r of the area of the dead cell region becomes relatively greater than in other time periods is after about 264 hours from the start of a cell culture process in the case of iPS cells of the 1383D6 cell line. Therefore, it is preferable that the image P used when the dead cell region extraction unit  200  extracts the dead cell region be an image P captured after 486 hours from the start of the culture process in the case of the iPS cells of the TkDA cell line and it is preferable that the image P used when the dead cell region extraction unit  200  extracts the dead cell region be an image P captured after 264 hours from the start of the culture process in the case of the iPS cells of the 1383D6 cell line. Also, the dead cell region area calculation unit  202  may obtain the area of the dead cell region in the image P of the cells of any one of cell lines including the TkDA cell line and the 1383D6 cell line captured at at least one point in time after about 486 hours and 264 hours from the start of the cell culture process. Also, the time such as 486 hours or 264 hours is an example and the present invention is not limited thereto. The image P used when the area of the dead cell region is calculated by the dead cell region area calculation unit  202  may be preferably an image captured in any time period as long as it is a time period when the effect quantity r of the area of the dead cell region is 0.5 or more. For example, it is also preferable that the time period be a time period when the effect quantity r of the area of the dead cell region in the image P of the cells of at least one of the TkDA cell line and the 1383D6 cell line is 0.5 or more. Also, the cell is not limited to the TkDA cell line or the 1383D6 cell line and cells of other cell lines may be determined by the determination unit  220 . For example, the dead cell region area calculation unit  202  may calculate the area of the dead cell region from images of cells of other cell lines captured in a time period when the effect quantity r of the area of the dead cell region in the image P of the cells of at least one of the TkDA cell line and the 1383D6 cell line is 0.5 or more. Also, the time period when effect quantities r of the area of the dead cell region in the images P of cells of both the TkDA cell line and the 1383D6 cell line are 0.5 or more is, for example, a time period of 6 to 9 hours, 63 to 69 hours, 78 to 81 hours, 90 to 96 hours, 186 to 201 hours, 207 to 210 hours, 222 hours, 228 to 261 hours, 270 hours, 276 to 282 hours, 294 hours, 300 hours, 306 to 348 hours, 366 to 369 hours, 375 to 492 hours, or the like from the start of the culture process as an example. Also, the determination unit  220  may not make the determination for the area of the dead cell region in the image P captured in the time period when the effect quantity r is 0.5 or more on the basis of the effect quantity r or may make the determination on the basis of effect quantities r of other values. 
       FIG.  32    is a diagram showing an example of the effect quantity of the area of the non-cell region. In  FIG.  32   , a change over time in the effect quantity r of the area of the non-cell region based on an image P obtained by imaging two types of iPS cells (TkDA and 1383D6) cultured in the culture medium is shown. As shown in  FIG.  32   , the time when the effect quantity r of the area of the non-cell region becomes relatively greater than in other time periods is after about 138 hours from the start of a cell culture process in the case of the iPS cells of the TkDA cell line and the time when the effect quantity r of the area of the non-cell region becomes relatively greater than in other time periods is after about 300 hours from the start of a cell culture process in the case of iPS cells of the 1383D6 cell line. Therefore, it is preferable that the image P used when the non-cell region extraction unit  190  extracts the non-cell region be an image P captured after 138 hours from the start of the culture process in the case of the iPS cells of the TkDA cell line and it is preferable that the image P used when the non-cell region extraction unit  190  extracts the non-cell region be an image P captured after 300 hours from the start of the culture process in the case of the iPS cells of the 1383D6 cell line. Also, the non-cell region area calculation unit  192  may obtain the area of the non-cell region in the image P of the cells of any one of cell lines including the TkDA cell line and the 1383D6 cell line captured at at least one point in time after about 138 hours and 300 hours from the start of the cell culture process. Also, the time such as 138 hours or 300 hours is an example and the present invention is not limited thereto. The image P used when the area of the non-cell region is calculated by the non-cell region area calculation unit  192  may be preferably an image captured in any time period as long as it is a time period when the effect quantity r of the area of the non-cell region is 0.5 or more. For example, it is also preferable that the time period be a time period when the effect quantity r of the area of the non-cell region in the image P of the cells of at least one of the TkDA cell line and the 1383D6 cell line is 0.5 or more. Also, the cell is not limited to the TkDA cell line or the 1383D6 cell line and cells of other cell lines may be determined by the determination unit  220 . For example, the non-cell region area calculation unit  192  may calculate the area of the non-cell region from images of cells of other cell lines captured in a time period when the effect quantity r of the area of the non-cell region in the image P of the cells of at least one of the TkDA cell line and the 1383D6 cell line is 0.5 or more. Also, the time period when effect quantities r of the area of the non-cell region in the images P of cells of both the TkDA cell line and the 1383D6 cell line are 0.5 or more is, for example, a time period of 72 hours, 270 to 300 hours, 306 to 345 hours, or the like from the start of the culture process as an example. Also, the determination unit  220  may not make the determination for the area of the non-cell region in the image P captured in the time period when the effect quantity r is 0.5 or more on the basis of the effect quantity r or may make the determination on the basis of effect quantities r of other values. 
       FIG.  33    is a diagram showing an example of the effect quantity of the area of the dying and dead cell region. In  FIG.  33   , a change over time in the effect quantity r of the area of the dying and dead cell region based on an image P obtained by imaging two types of iPS cells (TkDA and 1383D6) cultured in the culture medium is shown. As shown in  FIG.  33   , the time when the effect quantity r of the area of the dying and dead cell region becomes greatest is after about 387 hours from the start of a cell culture process in the case of the iPS cells of the TkDA cell line and the time when the effect quantity r of the area of the dying and dead cell region becomes greatest is after about 408 hours from the start of a cell culture process in the case of iPS cells of the 1383D6 cell line. Therefore, it is preferable that the image P used when the dying and dead cell region extraction unit  210  extracts the dying and dead cell region be an image P captured after 387 hours from the start of the culture process in the case of the iPS cells of the TkDA cell line and it is preferable that the image P used when the dying and dead cell region extraction unit  210  extracts the dying and dead cell region be an image P captured after 408 hours from the start of the culture process in the case of the iPS cells of the 1383D6 cell line. Also, the dying and dead cell region area calculation unit  212  may obtain the area of the dying and dead cell region in the image P of the cells of any one of cell lines including the TkDA cell line and the 1383D6 cell line captured at at least one point in time after about 387 hours and 408 hours from the start of the cell culture process. Also, the time such as 387 hours or 408 hours is an example and the present invention is not limited thereto. The image P used when the area of the dying and dead cell region is calculated by the dying and dead cell region area calculation unit  212  may be preferably an image captured in any time period as long as it is a time period when the effect quantity r of the area of the dying and dead cell region is 0.5 or more. For example, it is also preferable that the time period be a time period when the effect quantity r of the area of the dying and dead cell region in the image P of the cells of at least one of the TkDA cell line and the 1383D6 cell line is 0.5 or more. Also, the cell is not limited to the TkDA cell line or the 1383D6 cell line and cells of other cell lines may be determined by the determination unit  220 . For example, the dying and dead cell region area calculation unit  212  may calculate the area of the dying and dead cell region from images of cells of other cell lines captured in a time period when the effect quantity r of the area of the dying and dead cell region in the image P of the cells of at least one of the TkDA cell line and the 1383D6 cell line is 0.5 or more. Also, the time period when effect quantities r of the area of the dying and dead cell region in the images P of cells of both the TkDA cell line and the 1383D6 cell line are 0.5 or more is, for example, a time period of 3 to 18 hours, 63 to 102 hours, 111 to 135 hours, 159 to 300 hours, 306 to 348 hours, 360 to 492 hours, or the like from the start of the culture process as an example. Also, the determination unit  220  may not make the determination for the area of the dying and dead cell region in the image P captured in the time period when the effect quantity r is 0.5 or more on the basis of the effect quantity r or may make the determination on the basis of effect quantities r of other values. 
     Although the case where the effect quantity r is used as a method of identifying a suitable time used for the determination process of the determination unit  220  has been described above, the present invention is not limited thereto. For example, the control unit  101  may derive a receiver operating characteristic (ROC) curve on the basis of the number of successful groups n1 and the number of unsuccessful groups n2 in the image P related to a certain region among the images P stored in the storage unit  301  and identify a time of the image P when an area under the curve (AUC), which is a value representing the accuracy of separation of the successful group and the unsuccessful group, is close to “1” (i.e., the separation accuracy between the successful group and the unsuccessful group is high). In this case, the determination unit  220  performs a determination process for each region on the basis of the image P at a time when the AUC is high. 
     &lt;Regarding Operation of Image Determination Device  10 &gt; 
       FIG.  34    is a diagram showing an example of the operation of the image determination device  10  of the third embodiment. The paving stone region-specific average luminance value calculation unit  162  of the image determination device  10  calculates an average value of the luminance values in the paving stone region extracted by the paving stone region extraction unit  160  (step S 800 ). Subsequently, the adhesion region area calculation unit  172  calculates an area of the adhesion region extracted by the adhesion region extraction unit  170  (step S 802 ). Subsequently, the cell adhesion region area calculation unit  182  calculates an area of the cell adhesion region extracted by the cell adhesion region extraction unit  180  (step S 804 ). Subsequently, the non-cell region area calculation unit  192  calculates an area of the non-cell region extracted by the non-cell region extraction unit  190  (step S 806 ). Subsequently, the dead cell region area calculation unit  202  calculates an area of the dead cell region extracted by the dead cell region extraction unit  200  (step S 808 ). Subsequently, the dying and dead cell region area calculation unit  212  calculates an area of the non-cell region extracted by the dying and dead cell region extraction unit  210  (step S 810 ). 
     The determination unit  220  determines the quality of cell differentiation on the basis of information calculated by each functional unit (step S 812 ). For example, the determination unit  220  determines that the quality of cell differentiation is low when any one of a condition (1) that the average value of the luminance values in the paving stone region calculated by the paving stone region-specific average luminance value calculation unit  162  is less than the threshold value for the average value of the luminance values in the paving stone region indicated by the paving stone region-specific average luminance threshold value  301   a , a condition (2) that the value of the area of the adhesion region calculated by the adhesion region area calculation unit  172  is less than the threshold value for the area of the adhesion region indicated by the adhesion region area-specific threshold value  301   b , a condition (3) that the value of the area of the cell adhesion region calculated by the cell adhesion region area calculation unit  182  is less than the threshold value for the area of the cell adhesion region indicated by the cell adhesion region area-specific threshold value  301   c , a condition (4) that the value of the area of the non-cell region calculated by the non-cell region area calculation unit  192  is greater than or equal to the threshold value for the area of the non-cell region indicated by the non-cell region area-specific threshold value  301   d , a condition (5) that the value of the area of the dead cell region calculated by the dead cell region area calculation unit  202  is greater than or equal to the threshold value for the area of the dead cell region indicated by the dead cell region area-specific threshold value  301   e , and a condition (6) that the value of the area the dying and dead cell region calculated by the dying and dead cell region area calculation unit  212  is greater than or equal to the threshold value for the area of the dying and dead cell region indicated by the dying and dead cell region area-specific threshold value  301   f  is satisfied (step S 814 ) and determines that the quality of cell differentiation is high when any condition is not satisfied (step S 816 ). 
     A case where the quality of cell differentiation is high is, for example, a case where a ratio of the number of normal mature hepatocytes to the final number of cells is higher than or equal to a standard ratio (for example, 80%), a case where the bias of the shape of mature hepatocytes is less than a standard or the like, and a case where the quality of cell differentiation is low is, for example, a case where the ratio is less than the standard ratio, a case where the bias of the shape of mature hepatocytes is greater than or equal to the standard, or the like. 
     Also, the determination unit  220  may determine the quality of cell differentiation on the basis of the index value for determining the quality of differentiation in the time-lapse image before the cells form the paving stone region and the luminance value of the paving stone region in the time-lapse image after the cells form the paving stone region. Here, the index value for determining the quality of differentiation in the time-lapse image before the cells form the paving stone region is, for example, a value calculated for the above-described evaluation index before the paving stone region is formed. Values calculated for the evaluation index described above before the paving stone region is formed include, for example, the area of the adhesion region, the area of the cell adhesion region, the area of the non-cell region, the area of the dead cell region, and the area of the dying and dead cell region. 
     The determination unit  220  determines the quality of cell differentiation on the basis of, for example, a first condition, which is a condition indicating a magnitude relationship between an index value for determining the quality of differentiation in a time-lapse image before cells form a paving stone region and a threshold value for this index value, and a second condition, which is a condition indicating a magnitude relationship between the luminance value of the paving stone region in the time-lapse image after cells form the paving stone region and the paving stone region-specific average luminance threshold value  301   a.    
     The determination unit  220  determines that the quality of cell differentiation is low, for example, when both the first condition and the second condition are satisfied. In this case, the determination unit  220  determines that the quality of cell differentiation is high when at least one of the first condition and the second condition is not satisfied. Also, the determination unit  220  may determine that the quality of cell differentiation is low, for example, when at least one of the first condition and the second condition is satisfied. In this case, the determination unit  220  determines that the quality of cell differentiation is high when both the first condition and the second condition are not satisfied. Also, the above-described condition is an example and the determination unit  220  may reverse signs of the index value and the threshold value for the index value to determine the quality of cell differentiation. In this case, the determination unit  220  determines that the quality of cell differentiation is low when both the first condition and the second condition are not satisfied. 
     Here, the first condition is, for example, a condition (referred to as a condition (2a)) that the value of the area of the adhesion region calculated by the adhesion region area calculation unit  172  under the above-described condition (2) is set as a value calculated before cells form the paving stone region. Also, as another example, the first condition is referred to as a condition (a condition (3a)) that the value of the area of the cell adhesion region calculated by the cell adhesion region area calculation unit  182  under the above-described condition (3) is set as a value calculated before the cells form the paving stone region. As another example, the first condition is a condition (referred to as a condition (4a)) that the value of the area of the non-cell region calculated by the non-cell region area calculation unit  192  under the above-described condition (4) is set as a value calculated before the cells form the paving stone region. Also, as another example, the first condition is a condition (referred to as a condition (5a)) that the value of the area of the dead cell region calculated by the dead cell region area calculation unit  202  under the above-described condition (5) is set as a value calculated before the cells form the paving stone region. Also, as another example, the first condition is a condition (referred to as a condition (6a)) that the value of the area of the dying and dead cell region calculated by the dying and dead cell region area calculation unit  212  under the above-described condition (6) is a value calculated before the cells form the paving stone region. 
     The second condition is, for example, a condition (referred to as condition (1a)) that the average value of the luminance values in the paving stone region calculated by the paving stone region-specific average luminance value calculation unit  162  under the above-described condition (1) is set as a value calculated after cells form the paving stone region. 
     Also, the determination unit  220  may determine the quality of cell differentiation on the basis of a third condition that is a condition based on the index value for determining the quality of differentiation in the time-lapse image before the cells form the paving stone region and the luminance value of the paving stone region in the time-lapse image after the cells form the paving stone region. For example, the third condition is a condition indicating a magnitude relationship between a prescribed value and an average, a difference, a weighted average, a product, or a ratio between an index value for determining the quality of differentiation in the time-lapse image before cells form the paving stone region and a luminance value of the paving stone region in the time-lapse image after cells form the paving stone region. When the dimension of the index value for determining the quality of differentiation in the time-lapse image before cells form the paving stone region is different from the dimension of the luminance value of the paving stone region in the time-lapse image after the cells form the paving stone region, the average, the difference, or the weighted average is calculated by multiplying a factor for aligning both dimensions when the average, the difference, or the weighted average is calculated. 
     The third condition is, for example, that an average value between the value of the area of the adhesion region calculated by the adhesion region area calculation unit  172  on the basis of the time-lapse image before cells form the paving stone region and the average value of the luminance values in the paving stone region calculated by the paving stone region-specific average luminance value calculation unit  162  on the basis of the time-lapse image after cells form the paving stone region is smaller than a prescribed threshold value. Here, the value of the area of the adhesion region is multiplied by a prescribed factor so that the dimension matches the luminance value and the average associated with the average value of the luminance values in the paving stone region is calculated. Also, the determination unit  220  may reverse the signs of the index and the index value of the third condition to determine the quality of cell differentiation. 
     Among the above-described threshold values, the adhesion region area-specific threshold value  301   b , the cell adhesion region area-specific threshold value  301   c , the non-cell region area-specific threshold value  301   d , the dead cell region area-specific threshold value  301   e , and the dying and dead cell region area-specific threshold value  301   f  are examples of an index for determining the quality of differentiation before cells form the paving stone region. Also, among the above-described threshold values, the paving stone region-specific average luminance threshold value  301   a , the adhesion region area-specific threshold value  301   b , the cell adhesion region area-specific threshold value  301   c , the non-cell region area-specific threshold value  301   d , the dead cell region area-specific threshold value  301   e , and the dying and dead cell region area-specific threshold value  301   f  are examples of an index for determining the quality of differentiation after cells form the paving stone region. The average value of the luminance values in the paving stone region, the area of the adhesion region, the area of the cell adhesion region, the area of the non-cell region, the area of the dead cell region, and the area of the dying and dead cell region are examples of indices calculated in correspondence with these threshold values. 
     Therefore, the determination unit  220  determines the quality of cell differentiation on the basis of an index value (the area of the adhesion region, the area of the cell adhesion region, the area of the non-cell region, the area of the dead cell region, and the area of the dying and dead cell regions) calculated in correspondence with an index (the adhesion region area-specific threshold value  301   b , the cell adhesion region area-specific threshold value  301   c , the non-cell region area-specific threshold value  301   d , the dead cell region area-specific threshold value  301   e , and the dying and dead cell region area-specific threshold value  301   f ) for determining the quality of differentiation before cells form the paving stone region and an index value (the average value of the luminance values in the paving stone region, the area of the adhesion region, the area of the cell adhesion region, the area of the non-cell region, the area of the dead cell region, and the area of the dying and dead cell region) calculated in correspondence with an index (the paving stone region-specific average luminance threshold value  301   a , the adhesion region area-specific threshold value  301   b , the cell adhesion region area-specific threshold value  301   c , the non-cell region area-specific threshold value  301   d , the dead cell region area-specific threshold value  301   e , and the dying and dead cell region area-specific threshold value  301   f ) for determining the quality of differentiation after cells form the paving stone region. 
     Also, the determination unit  220  may be configured to determine that the quality of differentiation is low when a prescribed number (for example, two or more) of conditions among the conditions (1) to (6) and (1a) to (6a) are satisfied. Also, the determination unit  220  may be configured to determine that the quality of cell differentiation is high when the number of satisfied conditions among the conditions (1) to (6) and (1a) to (6a) is less than a prescribed number (for example, two). Also, the determination unit  220  may be configured to reverse the signs of the indices and index values of the conditions (1) to (6) and (1a) to (6a) to determine the quality of cell differentiation. 
     Also, the determination unit  220  may not determine the quality of cell differentiation under all the conditions (1) to (6) and (1a) to (6a). For example, the determination unit  220  may determine the quality of cell differentiation on the basis of a condition selected by the user via an input device (not shown) among the conditions (1) to (6) and (1a) to (6a). In this case, the user may select the condition for the determination unit  220  to determine the quality of cell differentiation from the conditions (1) to (6) and (1a) to (6a) on the basis of a value of the effect quantity r of each of the conditions (1) to (6) and (1a) to (6a) (for example, an average value or a maximum value of the effect quantity r at each time). Also, the user may not select a condition and the control unit  101  may select the condition for the determination unit  220  to determine the quality of cell differentiation from the conditions (1) to (6) and (1a) to (6a). In this case, the control unit  101  may select the condition for the determination unit  220  to determine the quality of cell differentiation from the conditions (1) to (6) and (1a) to (6a) on the basis of a value of the effect quantity r of each of the conditions (1) to (6) and (1a) to (6a) (for example, an average value, a maximum value, or the like of the effect quantity r at each time). Also, when the user or the control unit  101  selects the condition for the determination unit  220  to determine the quality of cell differentiation from the conditions (1) to (6) and (1a) to (6a), a region extraction unit corresponding to the selected condition among the region extraction units (the paving stone region extraction unit  160 , the adhesion region extraction unit  170 , the cell adhesion region extraction unit  180 , the non-cell region extraction unit  190 , the dead cell region extraction unit  200 , and the dying and dead cell region extraction unit  210 ) may be configured to perform a region extraction process. 
     The output unit  230  outputs a determination result of a determination process of the determination unit  220  (step S 900 ). 
     As described above, the image determination device  10  of the present embodiment can reduce the time and effort for determining a cell culture state by calculating an evaluation index (for example, a paving stone region luminance value, an adhesion region area, a cell adhesion region area, a non-cell region area, a photoreceptor cell region area, a dying and dead cell region area, and the like) according to a cell differentiation-inducing process and determining the quality of cell differentiation on the basis of the calculated evaluation index. 
     According to the above-described embodiment, a cell evaluation method for use in the image determination device  10  includes acquiring a first evaluation index (for example, the average value of the luminance values in the paving stone region, the area of the adhesion region, the area of the cell adhesion region, the area of the non-cell region, the area of the dead cell region, and the area of the dying and dead cell region) and a first index (for example, indices such as the paving stone region-specific average luminance threshold value  301   a , the adhesion region area-specific threshold value  301   b , the cell adhesion region area-specific threshold value  301   c , the non-cell region area-specific threshold value  301   d , the dead cell region area-specific threshold value  301   e , and the dying and dead cell region area-specific threshold value  301   f ) calculated using the first evaluation index with respect to comparative target cells in a culture process including a cell differentiation-inducing process; calculating a second index (for example, the average value of the luminance values in the paving stone region, the area of the adhesion region, the area of the cell adhesion region, the area of the non-cell region, the area of the dead cell region, and the area of the dying and dead cell region) on the basis of the first evaluation index with respect to evaluation target cells different from the comparative target cells; and evaluating differentiation of the evaluation target cells by comparing the first index with the second index. 
     The first evaluation index is acquired from a plurality of images associated with elapsed time from a start of the culture process and a high- or low-quality differentiation result. 
     In the cell evaluation method for use in the image determination device  10 , the culture process includes a plurality of processes and the first evaluation index according to the process is acquired. 
     Although a case where the paving stone region-specific average luminance threshold value  301   a , the adhesion region area-specific threshold value  301   b , the cell adhesion region area-specific threshold value  301   c , the non-cell region area-specific threshold value  301   d , the dead cell region area-specific threshold value  301   e , and the dying and dead cell region area-specific threshold value  301   f  are values predetermined by comparing indices based on an image P in which cells determined to become normal mature hepatocytes are imaged when the cell observer observes cells with indices based on an image P in which cells determined not to become normal mature hepatocytes are imaged when the cell observer observes cells has been described above, the present invention is not limited thereto. These threshold values are, for example, values acquired using images P of cells captured by the imaging device  34  at specific times in addition to images P captured after differentiation into normal mature hepatocytes is achieved or after differentiation into normal mature hepatocytes is not achieved. 
     Although the image determination device  10  determines the quality of cell differentiation on the basis of six evaluation indices (specifically, at least one evaluation index of six evaluation indices) obtained from the time-lapse images acquired during a period (for example, 21 days) of the first to fourth differentiation-inducing processes in the above-described embodiment, the present invention is not limited thereto. For example, the present invention can also be applied to processes including an undifferentiated state maintaining culture process for iPS cells before the first to fourth differentiation-inducing processes (i.e., processes from the undifferentiated state maintaining culture process to the first to fourth differentiation-inducing processes). As described above, the undifferentiated state maintaining culture process for iPS cells is a culture process (for example, 5 days) for increasing the number of iPS cells while maintaining the iPS cells in an undifferentiated state. In this case, the image determination device  10  determines the quality of cell differentiation on the basis of the above-described indices obtained from the time-lapse images acquired during a period (for example, 26 days) from the undifferentiated state maintaining culture process to the first to fourth differentiation-inducing processes. Here, because a method of determining the quality of cell differentiation based on each index is similar to the method as described above, description thereof will be omitted. Also, the present invention can be applied to some processes as well as all processes from the undifferentiated state maintaining culture process to the first to fourth differentiation-inducing processes. 
     Although the case where the time-lapse image after the cells form the paving stone region is a time-lapse image in which cells are determined to have formed the paving stone region by the paving stone region determination unit (the paving stone region extraction unit  160 ) has been described as an example in the present embodiment, the present invention is not limited thereto. The time-lapse image after cells form the paving stone region may be a time-lapse image for which the observer has predetermined that the cells have formed the paving stone region. In this case, the acquisition unit  110  acquires time-lapse images including a time-lapse image in which it is predetermined that cells have formed the paving stone region as the time-lapse image. Therefore, the time-lapse image after cells form the paving stone region may be a time-lapse image for which it has been predetermined that cells included in the time-lapse image acquired by the acquisition unit  110  have formed the paving stone region. In this case, the paving stone region extraction unit  160  may not have a function as the paving stone region determination unit. 
     Also, the index value based on a result of determining the quality of differentiation in the time-lapse image before cells form the paving stone region may be calculated after a process of capturing the time-lapse image is completed. In this case, the determination unit  220  determines the quality of cell differentiation on the basis of the captured time-lapse image after a process of capturing the time-lapse image is completed. Also, the index value based on the result of determining the quality of differentiation in the time-lapse image before cells form the paving stone region may be calculated immediately in a period of time until cells form the paving stone region. In this case, the determination unit  220  may determine the quality of cell differentiation for the time-lapse image after cells form the paving stone region before the process of capturing the time-lapse image is completed on the basis of the index value that has been calculated immediately. 
     Modified Example 3 
     Hereinafter, Modified Example 3 of the third embodiment described above will be described with reference to  FIGS.  35  to  40   . 
     Also, the evaluation index used for determining the quality of cell differentiation is not limited to the above-described six evaluation indices, and other evaluation indices may be used. For example, in the undifferentiated state maintaining culture process for iPS cells, a distance between iPS cells changes in accordance with the proliferation of the number of iPS cells. That is, the adhesion between cells is weakened. Because the distance between the iPS cells changes, the luminance value near the boundary between the adjacent iPS cells changes in the image (the phase difference image) acquired by the imaging device  34 . Here, the image determination device  10  may determine the quality of cell differentiation using an area of a region having a prescribed luminance value near a boundary between adjacent iPS cells as an evaluation index. The region having the prescribed luminance value near the boundary between the adjacent iPS cells has a muscle-shaped form and the region having the prescribed luminance value near the boundary between the adjacent iPS cells is referred to as a muscle region in the following description. Therefore, the image determination device  10  may determine the quality of cell differentiation using an area of the muscle region as an evaluation index. 
     &lt;Regarding Region within Image&gt; 
     Here, an original image which is an image before a region is extracted and a region within the original image will be described with reference to  FIGS.  35  to  38   .  FIG.  35    is a diagram showing an example of the original image. Mask images corresponding to regions shown in  FIGS.  36  to  38    are generated according to image processing to be described below from  FIG.  35   . The mask image is an image that masks a region other than a specific region of the original image. 
       FIG.  36    is a diagram showing an example of a sparse mask. The sparse mask is an image that masks a region other than a sparse region in the original image. The sparse region is a cell region having a low cell density around the colony. In  FIG.  36   , sparse regions R1-1 to R1-4 are shown as examples of the sparse region. In the sparse region, a boundary between the cytoplasm and the nucleus is clear. 
       FIG.  37    is a diagram showing an example of a dense mask according to the present embodiment. The dense mask is an image that masks a region other than a dense region in the original image. The dense region is a cell region having a high cell density around the colony. In  FIG.  37   , dense regions R2-1 to R2-4 are shown as examples of the dense region. In the dense region, a boundary between the cytoplasm and the nucleus is unclear. 
       FIG.  38    is a diagram showing an example of a muscle mask according to the present embodiment. The muscle mask is an image that masks a region other than the muscle region in the original image. As described above, the muscle region is a region having a prescribed luminance value near the cell boundary, in other words, a region formed by a gap between cells in the colony. In  FIG.  38   , sparse regions R3-1 to R3-4 are shown as examples of the sparse region. 
     &lt;Regarding Image Processing on Image P&gt; 
       FIG.  39    is a flowchart showing an example of an operation of the image determination device  10  of Modified Example 3. The acquisition unit  110  acquires an image P as an original image according to the processing of step S 910 . The processing of steps S 920  to S 9160  to be described below is executed by a muscle region extraction unit (not shown) in the control unit  101  of the image determination device  10 . 
     The muscle region extraction unit (not shown) performs a process of correcting the background of the image P acquired by the acquisition unit  110  (step S 920 ). The muscle region extraction unit (not shown) performs, for example, a process of flattening the background of the image P as a process of correcting the background. Subsequently, the muscle region extraction unit (not shown) performs a segmentation process for the image P whose background has been corrected (step S 930 ). In the segmentation process, the muscle region extraction unit (not shown) determines a cell region in the image P and a region other than the cell and excludes the region other than the cell from the image P. 
     Hereinafter, the muscle region extraction unit (not shown) generates a sparse mask according to the processing of steps S 940  to S 970 , generates a muscle mask according to the processing of steps S 9120  to S 9150 , and generates a dense mask according to the processing of steps S 980  to S 9160 . The muscle region extraction unit (not shown) executes the processing of steps S 940  to S 970 , the processing of steps S 9120  to S 9150 , and the processing of steps S 980  to S 9160  in parallel. 
     The muscle region extraction unit (not shown) applies a morphology filter to the image P processed in the processing of steps S 920  and S 930  (step S 940 ) and performs a process of smoothing the entire image P (step S 950 ). Subsequently, the muscle region extraction unit (not shown) performs a threshold value-specific process on the image P and extracts a region having a luminance value less than or equal to a luminance value of the sparse region (step S 960 ). The muscle region extraction unit (not shown) extracts a sparse region from the image P by extracting a region having a luminance value less than or equal to the luminance value of the sparse region from the image P. The muscle region extraction unit (not shown) generates a sparse mask on the basis of the sparse region extracted according to the processing of step S 960  (step S 970 ). 
     The muscle region extraction unit (not shown) applies a morphology filter to the image P processed in the processing of steps S 920  and S 930  (step S 9120 ). The muscle region extraction unit (not shown) clarifies the boundary of the muscle region by applying the morphology filter. The morphology filter is used to separate garbage and dying cells from a boundary between adjacent cells. The muscle region extraction unit (not shown) performs a process of flattening the background of the image P (step S 9130 ) and performs a threshold value-specific process (step S 9140 ). Here, the muscle region extraction unit (not shown) extracts a region having a luminance value greater than or equal to the threshold value TH4 and less than or equal to the threshold value TH5 as the muscle region in the threshold value-specific process in step S 9140 . The muscle region extraction unit (not shown) generates a muscle mask on the basis of the extracted muscle region (step S 9150 ). 
     The muscle region extraction unit (not shown) applies a morphology filter to the image P on which the processing of steps S 920  and S 930  has been performed (step S 980 ). Subsequently, the cell adhesion region extraction unit  180  performs a labeling process on a region clarified by the morphology filter (step S 990 ). The muscle region extraction unit (not shown) applies the morphology filter to the image P in which the missing pixels have been filled according to the labeling process (step S 9100 ). 
     The muscle region extraction unit (not shown) performs a subtraction process (step S 9110 ). In the subtraction process, the muscle region extraction unit (not shown) excludes the sparse region corresponding to the sparse mask generated in step S 970  and the muscle region corresponding to the muscle mask generated in step S 9150  from the entire image P. The muscle region extraction unit (not shown) generates a dense mask on the basis of a result of excluding the sparse region and the muscle region from the cell region of the image P. 
     When the processing of steps S 910  to S 9160  ends, the muscle region area calculation unit (not shown) of the control unit  101  calculates an area of the muscle region extracted by the muscle region extraction unit (not shown) as an evaluation index and supplies the evaluation index to the determination unit  220 . Also, because the other operations of the image determination device  10  are similar to those described above, description thereof will be omitted. 
     A series of processes of the threshold value-specific process, the smoothing process, the background flattening process, the labeling process, and the morphology filter process of the above-described muscle region extraction unit (not shown) are an example, and other existing image processing may be combined with the series of processes or may be replaced with a part of this series of processes. Also, the area of the cell boundary region may be calculated as an evaluation index not only in the undifferentiated state maintaining culture process for iPS cells but also in the other first to fourth differentiation-inducing processes. In this case, it is possible to suitably determine the quality of differentiation into hepatic endoderm cells particularly. 
     &lt;Relationship Between Muscle Region and Amount of Albumin&gt; 
     Here, a relationship between a muscle region in the undifferentiated state maintaining culture process and an amount of albumin (ALB) in the differentiation-inducing process will be described with reference to  FIG.  40   .  FIG.  40    is a diagram showing an example of the relationship between the muscle region and the amount of albumin. A graph G1 is a graph showing a ratio of the muscle region to the cell area in the undifferentiated state maintaining culture process with respect to the amount of albumin in the differentiation-inducing process. The amount of albumin is calculated in units of wells in which cells are cultured. 
     A point Q2 indicates a ratio of the muscle region to the cell area with respect to cells contained in a well having an albumin amount of 267.4 and this ratio is 44.0%. A point Q1 indicates a ratio of the muscle region to the cell area with respect to cells contained in a well having an albumin amount of 81.4, and this ratio is 33.0%. In the graph G1, a straight line L 1  showing a result of fitting a plurality of points indicating the ratio of the muscle region to the cell area with respect to the albumin amount by a straight line is shown. 
     As shown in the graph G1, the expression level of albumin in the differentiation-inducing process after the undifferentiated state maintaining culture process increases as the ratio of a muscle region to a cell area in the undifferentiated state maintaining culture process increases and the correlation between the two can be confirmed. Also, the correlation coefficient of the entire sample was 0.79. 
     In addition to the above-described evaluation index (the area of the muscle region in the undifferentiated state maintaining culture process), an index value indicating the cell adhesion state in the undifferentiated state maintaining culture process may be used for the evaluation index used for determining the quality of cell differentiation. Therefore, the index value based on the result of determining the quality of differentiation in the time-lapse image before cells form the paving stone region includes an index value indicating the area of the muscle region in the time-lapse image in a process in which the cells are maintained and cultured in an undifferentiated state or an index value indicating the cell adhesion state. 
     Also, the evaluation index used for determining the quality of cell differentiation is not limited to the eight evaluation indices including the above-described evaluation index (the area of the muscle region in the undifferentiated state maintaining culture process) and existing evaluation indices may be used. 
     Also, as the threshold value of each evaluation index used for determining the quality of cell differentiation, statistics of indices of a plurality of images P related to a series of culture processes stored in the storage unit  301  may be used and statistics of index values of a plurality of images P for which the observer determines that the quality of cell differentiation is high among images P stores in the storage unit  301  may be used. 
     That is, statistics for a plurality of first indices calculated using a plurality of first evaluation indices acquired from each of the plurality of images P may be used as the first index. Here, the plurality of images P may be images in which a result of evaluating the differentiation of an evaluation target cell indicates that the quality of the differentiation is evaluated as high. 
     Also, in the cell evaluation method for use in the image determination device  10  according to the above-described embodiment, the first evaluation index includes any one of: 
     (a) a luminance value of a region containing paving stone-shaped cells; 
     (b) an area in which the comparative target cells adhere to a culture vessel; 
     (c) an area in which the comparative target cells are densely packed in the culture vessel; 
     (d) an area of a region where there are no comparative target cells; 
     (e) an area of dying cells or dead cells among the comparative target cells; and 
     (f) a ratio of a perimeter of a cell of the comparative target cells to an area of the cell. 
     Also, the first index includes a value of the area of the muscle region and/or an index value indicating the cell adhesion state in the process of maintaining and culturing the cells in an undifferentiated state. 
     In another example, the first index is a value of the area of the cell boundary region. 
     Also, each part provided in the image determination device  10  in the above-described embodiment may be implemented by dedicated hardware or may be implemented by a memory and a microprocessor. 
     Also, each part of the image determination device  10  includes a memory and a CPU and its function may be implemented by loading a program for implementing the function of each part of the image determination device  10  into the memory and executing the program. 
     Also, a process is performed by recording a program for implementing the functions of the parts provided in the image determination device  10  on a computer-readable recording medium and causing a computer system to read and execute the program recorded on the recording medium. Also, the “computer system” used here may include an operating system (OS) and hardware such as peripheral devices. 
     Also, the “computer system” is assumed to include a homepage providing environment (or displaying environment) when a World Wide Web (WWW) system is used. 
     Also, the “computer-readable recording medium” refers to a storage device such as a flexible disc, a magneto-optical disc, a ROM, a portable medium such as a compact disc-ROM (CD-ROM), and a hard disk embedded in the computer system. Further, the “computer-readable recording medium” is assumed to include a computer-readable recording medium for dynamically retaining the program for a short period of time as in a communication line when the program is transmitted via a network such as the Internet or a communication circuit such as a telephone circuit and a computer-readable recording medium for retaining the program for a given period of time as in a volatile memory inside the computer system including a server and a client when the program is transmitted. Also, the above-described program may be a program for implementing some of the above-described functions. Further, the above-described program may be a program capable of implementing the above-described function in combination with a program already recorded on the computer system. 
     REFERENCE SIGNS LIST 
     
         
           10  Image determination device 
           110  Acquisition unit 
           130  Non-adhesion ratio-specific arithmetic unit 
           140  Area-specific arithmetic unit 
           150  Density-specific arithmetic unit 
           131  Non-adhesion area calculation unit 
           141  Area calculation unit 
           151  Area/perimeter calculation unit 
           132  Non-adhesion ratio calculation unit 
           142  Average area calculation unit 
           152  Density calculation unit 
           133  Non-adhesion ratio determination unit 
           143  Area determination unit 
           153  Density determination unit