Patent Publication Number: US-11398032-B2

Title: Image analysis system, culture management system, image analysis method, culture management method, cell group structure method, and program

Description:
TECHNICAL FIELD 
     This disclosure relates to an image analysis system which analyzes objects of culture such as cells and germs using images, and also relates to a culture system and a culture management system using the image analysis system. 
     BACKGROUND TECHNIQUE 
     Recently, according to progress in medical technology such as regenerative medicine and infertility treatment, there is an advance in technology of observing and evaluating situations of proliferation or inhibition of cells non-invasively and easily for objective cells (hereinafter referred to as “objective cells”) and objective cell groups (hereinafter referred to as “objective cell groups”). 
     Particularly, as a non-invasive method, there is recently known a technique of imaging objective cells and objective cell groups and analyzing the images to observe cells of interest. For example, there are known a technique of detecting each cell using time-lapse images in time series (e.g., Non-Patent References 1 and 2) and a tracking technique of tracking each cell (e.g., Non-Patent References 3 and 4). 
     Also, there are known an imaging device quantitatively grasping moving states of cells of interest using a plurality of images (e.g., Patent Reference 1), and a device calculating feature amounts indicating different forms of cells from a plurality of images and evaluating each cell based on the feature amounts (e.g., Patent Reference 2). 
     PRIOR ART REFERENCES 
     Patent References 
     
         
         Patent Reference 1: Japanese Patent Application laid-Open under No. 2009-229276 
         Patent Reference 2: Japanese Patent Application laid-Open under No. 2011-229410 
         Non-Patent Reference 1: Zhaozheng Yin, Takeo Kanade, Mei Chen: “Understanding the Phase Contrast Optics to Restore Artifact-Free Microscopy Images for Segmentation”, Medical Image Analysis 16(5): 1047-1062 (2012) 
         Non-Patent Reference 2: Zhaozheng Yin, Ryoma Bise, Mei Chen and Takeo Kanade: “Cell Segmentation in Microscopy Imagery Using a Bag of Local Bayesian Classifiers”, IEEE International Symposium on Biomedical Imaging (ISBI) 2010 
         Non-Patent Reference 3: Ryoma Bise, Zhaozheng Yin, Takeo Kanade: “Reliable Cell Tracking By Global Data Association”, IEEE International Symposium on Biomedical Imaging 2011 
         Non-Patent Reference 4: Takeo Kanade, Zhaozheng Yin, Ryoma Bise, Seungil Huh, Sungeun Eom, Michael Sandbothe and Mei Chen: “Cell Image Analysis: Algorithms, System and Applications”, IEEE Workshop on Applications of Computer Vision (WACV) 2011 
       
    
     SUMMARY OF THE INVENTION 
     Problem to be Solved by the Invention 
     However, not only recognizing a number of objective cells, but also confirming mixture of other cell species different from cell species of the objective cells is required because it affects treatment itself and its production process. In the devices of the above-mentioned Patent References, when cell groups subjected to evaluation and observation include plural kinds of cell groups having different attributes, it is difficult to easily estimate the number of the cell groups and a ratio of each cell group with keeping non-invasiveness. 
     The present invention is made to solve the above problem. Its object is to non-invasively estimate a mixing ratio of the cell group without affecting the treatment itself and its production process when the objective cell groups include plural kinds of cell groups having different attributes, and consequently to provide an image analysis system and a culture management system using the image analysis system, which are capable of accurately performing evaluation and a quality control of the objective cell groups and its production control with low cost. 
     Means for Solving the Problem 
     In order to solve the above problem, an image analysis system of this disclosure includes: 
     an acquisition unit configured to acquire data of plural objective images, in which objective cell groups including plural kinds of cell groups having different attributes are imaged, in time series; 
     a detection unit configured to detect a migration speed of each cell imaged in the objective images by analyzing the acquired plural objective images; 
     a generation unit configured to generate a distribution function or a distribution state of the migration speeds of the imaged objective cell groups based on the detected migration speed of each cell; and 
     an estimation unit configured to estimate a mixing ratio of each of the plural kinds of cell groups based on migration speed information, recorded in a storage unit in advance and including information of the migration speed of each of the plural kinds of cell groups, and the generated distribution function or the generated distribution state. 
     With this configuration, the image analysis system of this disclosure can estimate mixing ratios of the objective cell groups including plural kinds of cell groups having different attributes (e.g., attributes distinguished by characteristic or function such as doubling time) by analyzing images, and hence can estimate the mixing ratios of the objective cell groups non-invasively and accurately. 
     Accordingly, the image analysis system of this disclosure can estimate a ratio of a specific cell species (e.g., skeletal myoblast) by using the images when cell groups other than the specific cell group to be used are mixed in the objective cell groups. Thus, it is possible to evaluate quality of the objective cell groups, e.g., calculation of purity of the specific cell species included in the objective cell groups, and it is also possible to evaluate a culture state of the objective cell groups during or after the culture, e.g., cell death of the specific cell species or mutation to other cell species during culture. 
     As a result, the image analysis system of this disclosure can perform not only quality control of the objective cell groups and the specific cell species, but also production control of the cell species easily and accurately. 
     Also, in order to solve the above problem, this disclosure includes a culture management system which manages a state of objective cell groups including plural kinds of cell groups having different attributes in a predetermined culture period, comprising: 
     an acquisition unit configured to acquire data of plural objective images, in which objective cell groups including plural kinds of cell groups having different attributes are imaged, in time series at a predetermined timing in the culture period; 
     a detection unit configured to detect a migration speed of each cell imaged in the objective images by analyzing the acquired plural objective images; 
     a generation unit configured to generate a distribution function or a distribution state of the migration speeds of the imaged objective cell groups based on the detected migration speed of each cell; 
     an estimation unit configured to estimate a mixing ratio of each of the plural kinds of cell groups based on migration speed information, recorded in a storage unit in advance and including information of the migration speed of each of the plural kinds of cell groups, and the generated distribution function or the generated distribution state; 
     a determination unit configured to execute determination processing which determines whether or not the mixing ratio of each of the plural kinds of cell groups at the predetermined timing satisfies a predetermined mixing ratio condition; and 
     a notification unit configured to execute a predetermined notification of a result of the determination processing to a manager. 
     With this configuration, the culture management system of this disclosure can manage the culture of the objective cell groups having plural kinds of cell groups having different attributes, so as to prevent that cell species other than the specific cell species to be used increases in the objective cell groups more than a prescribed degree during the culture period and the cell species in the objective cell groups becomes unusable for regenerative medicine, for example. 
     Particularly, in case of the objective cell groups including cell groups of plural kinds of cell species having different doubling times, such as the objective cell groups collected from a living body (specifically muscle fibers) formed by the skeletal myoblasts and the fibroblasts, the culture management system can continue the culture of the cell groups of necessary cell species such as the skeletal myoblasts, while preventing the culture of the cell groups of unnecessary cell species such as the fibroblasts. Therefore, it becomes possible to culture useful objective cell groups including necessary cell species with a high mixing ratio. 
     Accordingly, the culture management system of this disclosure can perform quality control of the objective cell groups during the culture, and make the manager to control the culture of the objective cell groups while performing the quality control, thereby improving production efficiency of the objective cell groups. 
     Also, in order to solve the above problem, this disclosure includes a cell production method for producing at least a specific kind of cell group by controlling culture of objective cell groups collected from a living body and including plural kinds of cell groups having different attributes in a predetermined culture period, the method comprising the steps of: 
     executing inspection processing including determination processing which determines whether or not a mixing ratio of each of the plural kinds of cell groups included in the objective cell groups being cultured at a predetermined timing in the culture period satisfies a predetermined mixing ratio condition; and 
     executing inhibition processing of inhibiting culture of unnecessary cell species in the objective cell groups when the mixing ratio of each of the plural kinds of cell groups does not satisfy the mixing ratio condition, or executing preparation processing for executing the inhibition processing, 
     wherein the inspection processing comprising the steps of: 
     acquiring data of plural objective images, in which the objective cell groups including plural kinds of cell groups having different attributes are imaged, in time series at a predetermined timing; 
     detecting a migration speed of each cell imaged in the objective images by analyzing the acquired plural objective images; 
     generating a distribution function or a distribution state of the migration speeds of the imaged objective cell groups based on the detected migration speed of each cell; and 
     estimating a mixing ratio of each of the plural kinds of cell groups based on migration speed information, recorded in a storage unit in advance and including information of the migration speed of each of the plural kinds of cell groups, and the generated distribution function or the generated distribution state, and executing the determination processing based on the mixing ratio condition. 
     With this configuration, when the mixing ratio of the cell species other than the specific cell species, used for regenerative medicine for example, increases more than a specified degree, the cell group production method of this disclosure can inhibit the culture of cell groups of such cell species, thereby to surely culture the objective cell groups usable in the regenerative medicine. 
     Accordingly, the cell group production method of this disclosure can culture and produce the objective cell groups of proper quality, thereby improving production efficiency of the objective cell groups. 
     Effect of the Invention 
     This disclosure enables to estimate the mixing ratios of the objective cell groups non-invasively and accurately. Therefore, it is possible to perform quality control of the objective cell groups and the specific cell species as well as production control of the cell species easily and accurately. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram illustrating a configuration of a cell quality evaluation system according to a first embodiment of the present invention. 
         FIG. 2  is a block diagram illustrating a configuration of an image processing device of the cell quality evaluation system according to the first embodiment. 
         FIG. 3A  is a diagram for explaining mixing ratio estimation processing (mixed distribution function) executed by the image processing device according to the first embodiment. 
         FIG. 3B  is a diagram for explaining mixing ratio estimation processing (distribution function N 1 ) executed by the image processing device according to the first embodiment. 
         FIG. 3C  is a diagram for explaining mixing ratio estimation processing (distribution function N 2 ) executed by the image processing device according to the first embodiment. 
         FIG. 4  is a flowchart illustrating an operation of quality determination processing executed by the image processing device according to the first embodiment. 
         FIG. 5A  is a diagram for explaining simulation (mixed distribution function) using a log function distribution in the first embodiment. 
         FIG. 5B  is a diagram for explaining simulation (distribution function N 1 ) using a log function distribution in the first embodiment. 
         FIG. 5C  is a diagram for explaining simulation (distribution function N 2 ) using a log function distribution in the first embodiment. 
         FIG. 6A  is a diagram for explaining noise tolerance (noise level 0.0) in the first embodiment. 
         FIG. 6B  is a diagram for explaining noise tolerance (noise level 0.5) in the first embodiment. 
         FIG. 6C  is a diagram for explaining noise tolerance (noise level 1.0) in the first embodiment. 
         FIG. 7  is a graph illustrating a simulation result of an average error with respect to a noise level at an average migration speed in the first embodiment. 
         FIG. 8  is a diagram illustrating a configuration of a culture management system according to a second embodiment of the present invention. 
         FIG. 9  is a block diagram illustrating a configuration of an image processing device of the culture management system according to the second embodiment. 
         FIG. 10  is a flowchart illustrating an operation of culture management processing executed by the culture management device according to the second embodiment. 
         FIG. 11  is a diagram for explaining a principle of a cell production method according to a third embodiment of the present invention. 
         FIG. 12A  is a diagram illustrating a phase contrast image of objective cell groups of a mouse imaged by a phase contrast microscope, used to explain identifying fibroblasts from the objective cell groups in which the fibroblasts and skeletal myoblasts are mixed. 
         FIG. 12B  is a diagram illustrating an image of the objective cell groups in which the fibroblasts in the objective cell groups are fluorescently dyed, used to explain identifying the fibroblasts from the objective cell groups in which the fibroblasts and the skeletal myoblasts are mixed. 
     
    
    
     DESCRIPTION OF EMBODIMENTS TO EXERCISE INVENTION 
     Preferred embodiments of the present invention will be described below with reference to the attached drawings. The following embodiments are directed to a case where an image analysis system, a culture management system, an image analysis method, a culture management method, a cell production method and a program according to the present invention are applied to a cell quality evaluation system performing quality control of objective cell groups using images of the objective cell groups created by imaging cultured cell groups and a culture management system using the cell quality control system. However, the present invention is not limited to the following embodiments within the range including its technical idea. 
     [A] 1st Embodiment 
     [A1] Outline of a Cell Quality Evaluation System 
     First, a configuration and an outline of a cell quality evaluation system according to the first embodiment will be described with reference to  FIG. 1 .  FIG. 1  is a system configuration diagram illustrating a configuration of the cell quality evaluation system  1  according to this embodiment. 
     The cell quality evaluation system  1  according to this embodiment images objective cell groups placed in a specific container such as a dish and including plural kinds of cell groups having different attributes, and executes image analysis of the imaged objective cell groups (hereinafter referred to as “an objective image”). Thus, the cell quality evaluation system  1  estimates a mixing ratio of the plural kinds of cell groups, and evaluates the quality of the objective cell groups based on the estimated mixing ratio. 
     For example, when skeletal myoblasts collected from a living body of a person (donor) are subcultured, it is important to confirm a state (a culture state) of the skeletal myoblasts after culture, e.g., whether or not the number of the skeletal myoblasts after culture reaches a sufficient number for use in treatment, or whether or not the skeletal myoblasts of good quality are cultured (normally differentiated). 
     Particularly, even if cells collected from a living body (e.g., a human body) are cleaned and the cleaned cells are separated and recovered, not a few fibroblasts remain in the cell groups, and it is difficult to completely remove the fibroblasts to culture only the skeletal myoblasts as the objective cell. 
     Therefore, when the objective cells are cultured in such a state, the ratio of the skeletal myoblasts in the objective cell groups after culture becomes unknown. The skeletal myoblast has such a characteristic that its proliferation rate is slower than the fibroblast, and the number of the cells of the skeletal myoblast and the ratio of the skeletal myoblasts to whole cells cannot be specified until the culture ends. 
     Also, during subculture of the skeletal myoblasts, the same skeletal myoblast sometimes changes to a cell having a different attribute due to cell deaths, mutation of form, or maturity. In such a case, it is extremely important to recognize the ratio of appropriate skeletal myoblasts and inappropriate skeletal myoblasts for use in treatment or experiments. 
     On the other hand, by an invasive method such as a method using reagent, it is possible to estimate the mixing ratio of the plural kinds of cell groups to the objective cell groups and its quality. However, in this case, reagent affects the human body, and it becomes difficult to use the cells such as the skeletal myoblasts in treatment. 
     On the contrary, in a field of cell culture, even in cell groups having different attributes, the characteristic of each cell (i.e., specific predetermined feature amount) may be the same or may belong to the same range according to individual differences and a situation of culture environment. 
     Particularly, a speed at the time of cell migration known as a feature amount used for analyzing characteristic of each cell, i.e., a migration speed, can be easily obtained by tracking movement of each cell in the plural time-series images. However, the migration speed is not uniquely determined for each cell species having different attributes due to the state of the donor such as health at the time of collection, the culture environment or the culture state, or property of each cell, and is distributed in a certain range. 
     Therefore, in this embodiment, the migration speed is detected as a feature amount in each cell, which is easily analyzable from images, and the mixing ratio of each of the plural kinds of cell groups included in the objective cell groups is estimated based on the distribution of the detected migration speed and a pre-recorded migration speed information. 
     Specifically, the cell quality evaluation system  1  according to this embodiment includes an imaging device  10  which images the objective cell groups in time series to generate image data (hereinafter referred to as “objective image data”), a network  20 , and an image processing device  30  which estimates the mixing ratio of the objective cell groups imaged into the objective image data and evaluates the quality of the objective cell groups. 
     The imaging device  10  includes, for example, a communication function of connecting to the network  20  to transmit and receive data, an imaging function of acquiring prescribed images such as time-lapse images, and a microscope function of observing the cells. 
     Particularly, by the imaging function, the imaging device  10  takes still pictures of the objective cell groups placed on the dish at every fixed interval (e.g., 6 minutes or 12 minutes) to generate the objective image data as the time-lapse images, and transmits the generated objective image data to the image processing device  30  together with time information indicating an imaging time. 
     For example, the imaging device  10  includes an optical system, a CCDI sensor (Charge Coupled Device Image sensor) which converts optical images inputted from the optical system to an electric signal, and a generating unit which generates the image data based on the electric signal generated by the CCDI sensor. 
     Also, the imaging device  10  transmits the objective image data to the image processing device  30  directly by wired transmission or by wireless transmission via an access point not shown, using a predetermined communication standard such as LAN (Local Area Network). 
     For example, the network  20  may be a wired or wireless IP (Internet Protocol) network, or may be a network of a public telephone line including a portable telephone network. 
     In cooperation with the imaging device  10 , the image processing device  30  analyses the objective image data generated by the imaging device  10  to estimate the mixing ratios of plural kinds of cell groups, and evaluates the quality of the objective cell groups based on the estimated mixing ratios. 
     Specifically, the image processing device  30  executes processing of (hereinafter referred to as “quality determination processing): 
     (1) acquiring plural objective images of the objective cell groups from the imaging device  10  in time series, 
     (2) detecting a migration speed of each cell in the objective images by performing image analysis of the acquired plural objective images, 
     (3) generating a distribution function or a distribution state of the migration speeds of the imaged objective cell groups based on the detected migration speed of each cell, 
     (4) estimating a mixing ratio of each of the plural kinds of cell groups based on the pre-recorded migration speed information indicating migration speeds of each of the plural kinds of cell groups and the generated distribution function or the generated distribution state, and 
     (5) determining the quality of the objective cell groups as passed when the estimated mixing ratio satisfies a predetermined condition. 
     Particularly, in this embodiment, the image processing device  30  uses a normal distribution or a log normal distribution as the distribution function, based on the detected migration speed of each cell. 
     With the above configuration, the cell quality evaluation system  1  of this embodiment can estimate the mixing ratios of the objective cell groups non-invasively and accurately. Therefore, the cell quality evaluation system  1  can evaluate the quality of the objective cell groups such as calculation of a purity of certain cell species contained in the objective cell groups, and evaluate the culture state of the objective cell groups during or after the culture, such as cell deaths of a certain cell species or mutation to other cell species during culture. 
     Therefore, the cell quality evaluation system  1  of this embodiment can perform not only the quality control of the objective cell groups and certain objective cell species but also the production control of the cell species easily and accurately. 
     It is noted that the following description is directed to the case where the image processing device  30  estimates the mixing ratios of the plural kinds of cell groups based on the distribution function of the migration speed of the imaged objective cell groups and the pre-recorded migration speed information. 
     Also, while the following description uses the skeletal myoblasts and the fibroblasts at the skeletal muscle as the cell groups included in the objective cell groups, this embodiment may be applied to stem cells, the cell groups obtained by differentiation-inducing the stem cells, or target cells and other cells collected together with the target cells at the time of collecting from a body of a patient, for example: 
     (1) cell groups of corneal epithelial cells, corneal parenchymal cells and corneal endothelial cells at the cornea, 
     (2) cell groups of myocardial cells, vascular endothelial cells and fibroblasts at the cardiac muscle, 
     (3) cell groups of epidermal keratinocyte and fibroblasts at the skin, 
     (4) cell groups of retinal pigment epithelial cells, fibroblasts and vascular endothelial cells at the retina, and 
     (5) cell groups of mucosal epithelial cells, epidermal keratinocyte and fibroblasts at the mucous membrane. 
     Further, in this embodiment, the expression “different attributes” not only means the difference in the functions and the cell species like the skeletal myoblasts and the fibroblasts, but also means the difference in maturities indicating that the state of differentiation and undifferentiation or the intercellular adhesion advance to become a transplantable state, the difference in the external shapes such as shapes and sizes or structures, and the difference in presence/absence of mutation, injection of predetermined factor or fusion with other cell during culture. 
     [A2] Image Processing Device 
     Next, a configuration of the image processing device  30  of this embodiment will be described with reference to  FIG. 2 .  FIG. 2  is a block diagram illustrating blocks of the image processing device  30  of this embodiment. 
     Specifically, as shown in  FIG. 2 , the image processing device  30  of this embodiment includes a data recording unit  300  which records various data used when various programs are executed, a communication control unit  310  which transmits and receives various data such as time-series data of the objective cell groups transmitted from the imaging device  10 , a data processing unit  320  which executes quality determination processing based on the generated objective images, a display unit  340  including a liquid-crystal display, a display control unit  350  which controls the display unit  340 , an operation unit  370 , and a management control unit  380  which controls each block. The above blocks are connected with each other via a bus  39  to enable data transmission between each block. 
     The data recording unit  300  may be a HDD (Hard Disk Drive) for example, and includes an application recording unit  301  which records application programs for executing each processing such as quality determination processing, an image data recording unit  302  which records image data imaged and generated by the imaging device  10 , a reference data recording unit  303  which records various data such as migration speed information and thresholds used in the quality control processing, and a ROM/RAM  304  used as a work area during execution of each program. 
     Particularly, the image data recording unit  302  records objective image data acquired from the imaging device  10  and generated by imaging plural objective cell groups in time series for each dish (i.e., for each group). 
     The reference data recording unit  303  records the migration speed information of the cell groups included in the objective cell groups to be estimated. For example, in this embodiment, the reference data recording unit  303  records the values of an average speed and a variance (standard deviation) of the skeletal myoblast and the fibroblast as the migration speed information. 
     The communication control unit  310  is a certain network interface, and establishes a communication line with the imaging device  10  to transmit and receive various data acquired by the imaging device  10 . 
     The data processing unit  320  executes the following processing based on the applications for executing the quality determination processing recorded in the ROM/RAM  304 : 
     (1) processing of detecting the migration speed of each cell imaged in the objective images by executing image analysis of the plural objective images acquired from the imaging unit  10  in a time-series manner (hereinafter referred to as “migration speed detection processing”), 
     (2) processing of generating the distribution function related to the migration speed of the imaged objective cell groups based on the detected migration speed of each cell (hereinafter referred to as “distribution function generation processing”), 
     (3) processing of estimating the mixing ratio of each of the plural kinds of cell groups based on the pre-recorded migration speed information and the generated distribution function (hereinafter referred to as “mixing ratio estimation processing”), and 
     (4) processing of determining the quality of the objective cell groups as passed when the estimated mixing ratio satisfies the predetermined condition (hereinafter referred to as “pass determination processing”). 
     Particularly, by executing the applications, the data processing unit  320  realizes a detection processing unit  321  which executes the migration speed detection processing, a generation processing unit  322  which executes the distribution function generation processing, an estimation processing unit  323  which executes the mixing ratio estimation processing and a determination unit  324  which executes the pass determination processing. 
     For example, the detection processing unit  321  of this embodiment constitutes the acquiring unit of the present invention together with the communication control unit  310 , and constitutes the detection unit of the present invention. Also, for example, the generation processing unit  322  of this embodiment constitutes the generation unit of the present invention, and the estimation processing unit  323  constitutes the estimation unit of the present invention. Further, for example, the determination processing unit  324  of this embodiment constitutes the determination unit of the present invention. 
     The detail of each block in the data processing unit  320  of this embodiment will be described later. 
     The display unit  340  includes a panel of liquid-crystal elements or EL (Electro Luminescence) elements, and displays certain images based on the display data generated by the display control unit  350 . 
     Under the control of the management control unit  380  and the data processing unit  320 , the display control unit  350  generates drawing data necessary to make the display unit  340  draw certain images, and outputs the generated drawing data to the display unit  340 . 
     The operation unit  370  includes various kinds of confirmation buttons, operation buttons for inputting operation commands and numeric keypads formed by a touch sensor provided on the display unit  340 , and is used for each operation. 
     The management control unit  380  mainly includes a central processing unit (CPU), and performs total management control of the image processing unit  30  and other various controls by executing the programs. 
     The ROM/RAM  304  records various programs necessary for the operation of the image processing device  30 . Also, the ROM/RAM  304  records various applications to be executed by the data processing unit  320  and the management control unit  380 . The ROM/RAM  340  is used as a work area during execution of each program. 
     [A3] Data Processing Unit 
     Next, the detail of the data processing unit  320  in the image processing device  30  of this embodiment will be described with reference to  FIGS. 3A to 3C .  FIGS. 3A to 3C  are diagrams for explaining the mixing rate estimation processing (the mixed distribution function, the distribution function N 1  or the distribution function N 2 ) executed by the estimation processing unit  323  of this embodiment. 
     For example, while using the technique described in the above-mentioned Non-Patent References 1 to 4, the detection processing unit  321  acquires a plurality of time-series objective images (the time-lapse images) of the objective cell groups placed on the dish and imaged by the imaging device  10  from the image data recording unit  302 , identifies tracking of each cell included in each objective image by analyzing each of the acquired objective images, and executes the migration speed detection processing which detects the migration speed of each cell. 
     Specifically, the detection processing unit  321  detects an area having high luminance and surrounded by Halo, which is a bleeding of light appearing at the circumference of the image, as a cell by using the time-lapse images (phase contrast images) for each of a predetermined time interval and plural time-series time-lapse images, and detects cell divisions based on the variation of the cell shape using a probability model such as EDCRF. 
     Particularly, the detection processing unit  321  recognizes partially-overlapped cell shapes based on the cells detected in the immediately preceding frame to specify a moved position (coordinates (x,y) in the objective image) of the same cell, and then calculates a movement distance of each cell based on the coordinates in the previous and following objective images in time series. 
     Then, the detection processing unit  321  detects the migration speed of each cell based on the calculated movement distance and the time difference of the objective images, and stores the detected migration speed of each cell in the ROM/RAM  304 . 
     In this embodiment, it is sufficient to detect the migration speeds of the cells of about 1000 samples. For example, when the objective cell groups include 100 cells (or it is supposed that the objective cell groups include 100 cells), the detection processing unit  321  may execute the migration speed detection processing based on the objective images (time-lapse images) of about 10 frames. 
     Also, the detection processing unit  321  may identify each cell in each objective image by a manual operation of a worker using the operation unit  370 , and may compare the plural times-series objective images to identify the moved position of the same cell. 
     The generation processing unit  322  executes the distribution function generation processing which calculates the distribution function of the normal distribution or the log normal distribution of the detected migration speed of each cell. 
     Specifically, supposing that the migration speed of each cell becomes the normal distribution or the log normal distribution, the generation processing unit  322  calculates an average value and a variance value of the migration speed of each cell, and calculates the distribution function of the normal distribution or the log normal distribution using the average values and the variance values thus calculated. 
     For example, the generation processing unit  322  calculates the average value “μ” and the variance value “σ” in case of supposing the normal distribution of the migration speed of each cell, or calculates the average value “μ x ” and the variance value “σ x ” in the log normal distribution, and calculates the normal distribution function N 1 (x) expressed by the equation (1) or the log normal distribution function N 2 (x) expressed by the equation (2) as the mixed distribution function. 
     
       
         
           
             
               
                 
                   
                     
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     The estimation processing unit  323  executes the mixing ratio estimation processing for calculating the mixing ratio of the cell group based on the normal distribution function or the log normal distribution function thus calculated (i.e., the mixed distribution function) and the migration speed information of the cell groups included in the objective cell groups to be estimated and pre-recorded in the reference data recording unit  303 . 
     Normally, since the migration speed of each cell group is independent (not dependent) from the migration speed of other cell group, it is supposed that the migration speed of each cell is in accordance with the distribution of the migration speed of each cell group, specifically the normal distribution or the log normal distribution. In the observed mixed distribution (specifically, the average and the variance) of each cell group, if a basic distribution (specifically, the average and the variance) of each cell group included in the objective cell groups is known, the mixing ratio can be estimated by statistical processing using a predetermined algorithm. 
     Therefore, the estimation processing unit  323  estimates the mixing ratio of each of the plural kinds of cell groups based on the average speed and the variation (the standard deviation) serving as the migration speed information of each cell group included in the objective cell groups to be estimated, and the normal distribution function N 1 (x) of the equation (1) or the log normal distribution function N 2 (x) of the equation (2) calculated by the generation processing unit  322 . 
     Specifically, in the case where the mixing ratio is calculated for two cell groups, i.e., the cell group A (e.g., the skeletal myoblast) and the cell group B (e.g., the fibroblast) and the normal distribution function (i.e., the function indicating the mixed distribution) calculated by the generation processing unit  322  is the mixed distribution function f(x) shown in  FIG. 3A , the estimation processing unit  323  calculates the variables of the mixing ratio “   A ” and “   B ” satisfying the equations (3) and (4) based on a predetermined algorithm, and determines the ratio of the distribution functions shown in  FIGS. 3B and 3C . It is noted that the equation (3) is an equation of the mixed distribution function f(x) in case of the normal distribution function. 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           f 
                           ⁡ 
                           
                             ( 
                             x 
                             ) 
                           
                         
                         = 
                           
                         ⁢ 
                         
                           
                             
                               π 
                               A 
                             
                             ⁢ 
                             
                               
                                 N 
                                 1 
                               
                               ⁡ 
                               
                                 ( 
                                 
                                   x 
                                   A 
                                 
                                 ) 
                               
                             
                           
                           + 
                           
                             
                               π 
                               B 
                             
                             ⁢ 
                             
                               
                                 N 
                                 2 
                               
                               ⁡ 
                               
                                 ( 
                                 
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                                   B 
                                 
                                 ) 
                               
                             
                           
                         
                       
                     
                   
                   
                     
                       
                         = 
                           
                         ⁢ 
                         
                           
                             
                               π 
                               A 
                             
                             ⁢ 
                             
                               1 
                               
                                 
                                   2 
                                   ⁢ 
                                   π 
                                   ⁢ 
                                   
                                       
                                   
                                   ⁢ 
                                   
                                     σ 
                                     A 
                                     2 
                                   
                                 
                               
                             
                             ⁢ 
                             
                               exp 
                               ( 
                               
                                 - 
                                 
                                   
                                     
                                       ( 
                                       
                                         x 
                                         - 
                                         
                                           μ 
                                           A 
                                         
                                       
                                       ) 
                                     
                                     2 
                                   
                                   
                                     2 
                                     ⁢ 
                                     
                                       σ 
                                       A 
                                       2 
                                     
                                   
                                 
                               
                               ) 
                             
                           
                           + 
                           
                             
                               π 
                               B 
                             
                             ⁢ 
                             
                               1 
                               
                                 
                                   2 
                                   ⁢ 
                                   π 
                                   ⁢ 
                                   
                                       
                                   
                                   ⁢ 
                                   
                                     σ 
                                     B 
                                     2 
                                   
                                 
                               
                             
                             ⁢ 
                             
                               exp 
                               ( 
                               
                                 - 
                                 
                                   
                                     
                                       ( 
                                       
                                         x 
                                         - 
                                         
                                           μ 
                                           B 
                                         
                                       
                                       ) 
                                     
                                     2 
                                   
                                   
                                     2 
                                     ⁢ 
                                     
                                       σ 
                                       B 
                                       2 
                                     
                                   
                                 
                               
                               ) 
                             
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
             
               
                 
                   
                       
                   
                   ⁢ 
                   
                     
                       
                         π 
                         A 
                       
                       + 
                       
                         π 
                         B 
                       
                     
                     = 
                     1 
                   
                 
               
               
                 
                   ( 
                   4 
                   ) 
                 
               
             
           
         
       
     
     In this embodiment, the average values “μ A ” and “μ B ” and the variance values “σ A ” and “σ B ” in the equation (3) are known from the migration speed information. For example, when the normal distribution function N 1 (x A ) is skeletal myoblast, “μ A ” is 29.72857 μm/frame and “σ A ” is 6.602264. When the normal distribution function N 2 (x B ) is fibroblast, “μ B ” is 22.5 μm/frame and “σ B ” is 8.921323. 
     In this embodiment, as the predetermined algorithm, the estimation processing unit  323  uses EM (Expectation Maximization) algorithm which is one of maximum likelihood methods. Specifically, the estimation processing unit  323  calculates the parameter variables “   A ” and “   B ” at which the log likelihood function ln(p) of the equation (5) becomes maximum.
 
ln  p ( X |μ,σ,π)=Σ m=1   M  ln {Σ k=1   K π k   N ( x   n |μ,σ)}  (5)
 
     Particularly, in the equation (5), the estimation processing unit  323  first calculates an initialized value of the variable “   k ” using the average value “μ” and the variance value “σ”. Then, the estimation processing unit  323  calculates a burden rate “γ(z nk )” at the k-th variable with respect to the n-th factor (i.e., corresponding normal distribution function N(x)), calculates next variable “   k   new ”, and calculates the parameter variables “   A ” and “   B ” at which the log likelihood function ln(p) of the equation (5) becomes maximum therefrom. Note that the burden rate “γ(z nk )” is given by the equation (6), and the effective value “   k   new ” is given by the equation (7). 
     
       
         
           
             
               
                 
                   
                     γ 
                     ⁡ 
                     
                       ( 
                       
                         z 
                         nk 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       
                         π 
                         k 
                       
                       ⁢ 
                       
                         N 
                         ⁡ 
                         
                           ( 
                           
                             
                               
                                 x 
                                 n 
                               
                               | 
                               
                                 μ 
                                 k 
                               
                             
                             , 
                             
                               σ 
                               k 
                             
                           
                           ) 
                         
                       
                     
                     
                       
                         ∑ 
                         
                           j 
                           = 
                           1 
                         
                         K 
                       
                       ⁢ 
                       
                         
                           π 
                           j 
                         
                         ⁢ 
                         
                           N 
                           ⁡ 
                           
                             ( 
                             
                               
                                 
                                   x 
                                   n 
                                 
                                 | 
                                 
                                   μ 
                                   j 
                                 
                               
                               , 
                               
                                 σ 
                                 j 
                               
                             
                             ) 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
             
               
                 
                   
                     π 
                     k 
                     new 
                   
                   = 
                   
                     
                       
                         N 
                         k 
                       
                       M 
                     
                     = 
                     
                       
                         
                           ∑ 
                           
                             n 
                             = 
                             1 
                           
                           N 
                         
                         ⁢ 
                         
                           γ 
                           ⁡ 
                           
                             ( 
                             
                               z 
                               nk 
                             
                             ) 
                           
                         
                       
                       M 
                     
                   
                 
               
               
                 
                   ( 
                   7 
                   ) 
                 
               
             
           
         
       
     
     It is noted that “k” represents an index of the model distribution function (the normal distribution or the log normal distribution serving as a basis), and “K” represents its number. “N k ” represents an effective number of points assigned to the k-th cluster. The variable “   k ” is a weight of the k-th model distribution function, and a random value based on an appropriate ratio is used as the initial value of “   k ”. “M” represents a number of samples. 
     The determination processing unit  324  determines whether or not the ratio of the objective cell to the objective cell groups, estimated by the estimation processing unit  323 , satisfies a predetermined condition (e.g., equal to or larger than a constant value), and executes the pass determination processing of displaying the determination result on the display unit  340  in cooperation with the display control unit  350 . 
     Specifically, in this embodiment, the determination processing unit  324  determines whether or not the ratio of the cell group serving as a target in the mixed distribution function generated by the generation processing unit  322 , i.e., the skeletal myoblast, to the whole objective cell groups satisfies the predetermined condition (e.g., equal to or larger than a constant ratio). Then, the determination processing unit  324  determines “Passed” when the predetermined condition is satisfied, and determines “Failed” when the predetermined condition is not satisfied. The determination processing unit  324  displays the Result on the Display Unit  340 . 
     [A4] Operation of Cell Quality Evaluation System 
     Next, description will be given of the operation of the quality determination processing executed by the image processing device  30  of the cell quality evaluation system  1  according to this embodiment with reference to  FIG. 4 .  FIG. 4  is a flowchart showing the operation of the quality determination processing executed by the image processing device  30  of the cell quality evaluation system  1  according to this embodiment. 
     In this operation, it is supposed that the data recording unit  300  stores, in advance, the plural objective image data acquired in time series and the migration speed information of the plural kinds of cell groups having different attributes and included in the objective cell groups of the objective images. 
     Also, in this operation, the migration speed detection processing is executed by an automatic tracking. 
     First, the detection processing unit  321  detects the start of the quality determination processing based on the operation to the operation unit  370  including selection of objective images (step S 101 ), and acquires plural times-series objective images of specific objective cell groups selected (step S 102 ). 
     Then, the detection processing unit  321  identifies the tracking of each cell included in the imaged objective cell groups, and detects the migration speed of each cell (step S 103 ). Specifically, when the objective cell groups include 100 cells (or it is supposed that the objective cell groups include 100 cells), the detection processing unit  321  acquires the objective images of about 10 frames. 
     Next, the generation processing unit  322  calculates the average and the variance of the migration speed of each detected cell, and calculates the distribution function of the log normal distribution (the mixed distribution function) based on the averages and the variances thus calculated (step S 104 ). 
     Next, the estimation processing unit  323  reads out the migration speed information (the averages and the variances) of the corresponding cell groups from the reference data recording unit  303  (step S 105 ), and calculates the variable “ ” of the mixing ratio satisfying the equations (3) and (4) based on the read-out migration speed information and the calculated mixed distribution function according to a predetermined algorithm to identify the ratio of the mixed distribution function (step S 106 ). 
     Next, the determination processing unit  324  executes the pass determination processing which determines whether or not the ratio of the objective cell to the objective cell groups satisfies the predetermined condition (equal to or larger than a constant value) (step S 107 ). Specifically, the determination processing unit  324  determines whether or not the predetermined condition is satisfied. The determination processing unit  324  determines “Passed” when the ratio of the objective cell to the objective cell groups satisfies the predetermined condition, and determines as “Failed” when the ratio does not satisfy the predetermined condition. 
     Finally, the determination processing unit  324  displays the determination result, i.e., the result of “Passed” or “Failed” on the display unit  340  in cooperation with the display control unit  350  (step S 108 ), and ends the operation. 
     [A5] Simulation Results 
     [A5.1] Estimating Mixing Ratio 
     Next, simulation results of this embodiment will be described with reference to  FIGS. 5A to 5C .  FIGS. 5A to 5C  are diagrams for explaining the simulation using the log distribution function according to this embodiment (the mixed distribution function, the distribution function N 1  or the distribution function N 2 ). 
     This simulation is executed in the objective cell groups including the skeletal myoblast and the fibroblast. Particularly, this simulation detects the migration speed of each cell mixed by a predetermined mixing ratio (i.e., the cell whose kind of the attribute is unknown), and estimates the mixing ratio of each cell group based on the detection result and the migration speed information of the skeletal myoblast and fibroblast detected in advance. 
     Specifically, in this simulation, the mixing ratio and the number of samples are changed, and the average error and the standard deviation are calculated when the simulation is carried out 100 times. Particularly, this simulation uses the result obtained by detecting the migration speeds of 100 cells per one frame. For example, when the number of samples is “1000”, this simulation uses the result of detecting the migration speed of each cell for 10 frames, and calculates the ratio of the cell group of each cell species having different attributes by using the above-mentioned EM algorithm. 
     The simulation result using the normal distribution function is shown in TABLE-1, and the simulation result using the log normal distribution function is shown in TABLE-2 For example, as the simulation result using the normal distribution function, the distributions shown in  FIGS. 3B and 3C  are obtained for the mixed distribution shown in  FIG. 3A . As the simulation result using the log normal distribution function, the distributions shown in  FIGS. 5B and 5C  are obtained for the mixed distribution shown in  FIG. 5A . 
     In this simulation, as the migration speed information, the migration speed “μ A ” is 29.72857 μm/frame and the variance “σ A ” is 60602264 for the skeletal myoblast, and the migration speed “μ B ” is 22.5 μm/frame and the variance “σ B ” is 8.921323 for the fibroblast. This simulation is implemented without noise. 
     
       
         
           
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 
                   
                 
                 
                   
                 
                 
                   
                 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 100 
                 0.0735 
                 0.0619 
               
               
                 500 
                 0.0332 
                 0.0273 
               
               
                 1000 
                 0.0238 
                 0.0152 
               
               
                 5000 
                 0.0204 
                 0.0803 
               
               
                 10000 
                 0.009 
                 0.0063 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 
                   
                 
                 
                   
                 
                 
                   
                 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 100 
                 0.0439 
                 0.0394 
               
               
                 500 
                 0.0220 
                 0.0171 
               
               
                 1000 
                 0.0157 
                 0.0139 
               
               
                 5000 
                 0.0073 
                 0.0057 
               
               
                 10000 
                 0.0050 
                 0.0040 
               
               
                   
               
            
           
         
       
     
     As described above, when the normal distribution function is used, the maximum error is smaller than 7%, and it can be said that certain accuracy is ensured. Particularly, when the number of samples is larger than 1000, the error is smaller than 2.5%, and accuracy is sufficient. 
     When the log normal distribution function is used, accuracy is improved in all numbers of samples compared with the case of using the normal distribution function, and more favorable result is obtained. The reason is presumed as follows. In the normal distribution, for the movement distance used to calculate the migration speed of each cell, “minus” movement distances are allowed. On the other hand, in the log normal distribution, all the movement distances can be handled as “plus” movement distances, and this difference appears to be reflected to accuracy. 
     It is sufficient that the determination processing unit  324  in this embodiment determines whether or not the ratio of the objective cell to the objective cell groups, estimated in consideration that the above error is predicted, satisfies the predetermined condition (equal to or larger than a constant value). For example, when the error is predicted to be equal to or smaller than 2.5%, the determination processing unit  324  may determine the range within ±2.5% from the predetermined condition as a “Passed” range. However, since this “2.5%” is an average error, the “Passed” range may be broadened to ±3.0%, for example. 
     [A5.2] Noise Tolerance 
     Next, description will be given of variation of estimation accuracy when noise exists in this embodiment, with reference to  FIGS. 6A to 6C  and  FIG. 7 .  FIGS. 6A to 6C  are diagrams for explaining noise tolerance (noise levels 0.0, 0.5 and 1.0) of this embodiment and  FIG. 7  shows graphs illustrating simulation result of an average error with respect to the noise level at the average migration speed. 
     In this embodiment, it can be assumed that the donor of each cell to be detected in the objective cell groups is different from the donor of the pre-recorded migration speed information, that the migration speed itself is different due to a physical condition even if the donor of the pre-recorded migration speed information is identical to the donor from whom the objective cell is collected, or that the distribution ranges of the attribute (such as values) of mixed cells are largely overlapped. 
     Therefore, this simulation verifies the measurement errors in a case where noise is included in the migration speed of the cell to be actually detected and its distribution range (i.e., the average and the variance). 
     Specifically, similarly to the above-described simulation, this simulation is implemented for the objective cell groups including the skeletal myoblast and the fibroblast, and presents the result in a case where noise exists in each of the average migration speeds of the cell groups included in the objective cell groups. 
     Also, this simulation defines the noise level by the equation (8). Particularly, as shown in  FIG. 6A , when the noise level “L” is “0.0”, the averages of the migration speeds of the skeletal myoblast and the fibroblast actually included in the objective cell groups coincide. As shown in  FIG. 6B , when the noise level “L” is “0.5”, the difference of the averages become a half of the difference value in this embodiment. As shown in  FIG. 6C , when the noise level “L” is “1.0”, there is a difference of normal value for the averages of the migration speeds. 
     
       
         
           
             
               
                 
                   
                     
                       
                         m 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         1 
                       
                       - 
                       
                         m 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         2 
                       
                     
                     2 
                   
                   × 
                   L 
                 
               
               
                 
                   ( 
                   8 
                   ) 
                 
               
             
           
         
       
     
     As the supposed migration speeds, as described above, the migration speed “μ A ” is m1=29.72857 μm/frame and the variance “σ A ” is 60602264 for the skeletal myoblast, and the migration speed “μ B ” is m2=22.5 μm/frame and the variance “σ B ” is 8.921323 for the fibroblast. 
     Also, in this simulation, for each noise level L from “0” to “1.0” by “0.05” interval, the number of samples (1000 samples) is fixed and the mixing ratio is varied. The simulation is implemented 100 times with adding the noise based on the equation (8) using the above-mentioned EM algorithm, and the average errors at the mixing ratios are obtained. 
       FIG. 7  illustrates result of this simulation in the migration speed and the average error. If the average error is smaller than 10%, it can be said that accuracy is ensured. As the noise level satisfying the reliable range (95%), the noise level up to about “0.2” is within an allowable range. 
     Thus, even if there occurs various noises such as the difference of donors or physical condition of the donors, if the noise is smaller than a constant noise level, it is possible to estimate the mixing ratio in the objective cell groups. 
     [A6] Modified Examples 
     [A6.1] 1st Modified Example 
     In the above embodiment, the imaging device  10  and the image processing device  30  may be placed or used in the same room or in the same site. Instead, each of the imaging device  10  and the image processing device  30  may be placed or used at remote places such as foreign countries to implement the above-described processing. 
     The image processing device  30  may execute the quality determination processing using a database connected via the network  20 , or may be formed by one or plural devices. When the database is used, the database performs a part of the function of the data recording unit  300  (e.g., recording the objective images or the migration speed information). 
     [A6.2] 2nd Modified Example 
     In the above embodiment, the cell quality evaluation system is formed by the imaging device  10  and the image processing device  30 . However, the cell quality evaluation system of stand-alone type may be realized by providing an image data generation unit including a scanner and an imaging function. In that case, the image data generation unit constitutes the acquisition unit of the present invention, for example. 
     [A6.3] 3rd Modified Example 
     The above embodiment estimates the mixing ratios of the two kinds of objective cell groups such as the skeletal myoblast and the fibroblast. However, the present invention is applicable to the objective cell groups in which plural cell groups having different attributes of three or more kinds are mixed. 
     [A6.4] 4th Modified Example 
     In the above embodiment, the EM algorithm is used to calculate the ratio of the mixed distribution function. However, it may be solved as a least squares method problem with constraint conditions, without calculating the mixed distribution function of the detected migration speed of each cell. 
     For example, as a least squares method problem with constraint conditions, it is possible to assume that the mixed distribution is a sum of weights (i.e., π A  and π B ) of basic distributions (i.e., the distribution A of the skeletal myoblast and the distribution B of the fibroblast in the above embodiment), as a histogram having predetermined number of bins. Therefore, the mixed distribution “S” and the basic distributions “A” and “B” may be expressed by p-dimensional vector when the number of bins is “q”, and it is possible to calculate the ratio (i.e., π A  and π B ) with which “P” in the equation (10) becomes minimum based on the equation (9). 
     Namely, while the problem of calculating the weights π A  and π B  to achieve the equation (9) is considered, coincident value does not necessarily exists. Therefore, the weights are estimated by solving the problem of satisfying the constraint that the weights are positive and the sum of the weights is 1 and making the square error between the observed distribution S and the estimated combined distribution (π A A+π B B) becomes minimum. 
     
       
         
           
             
               
                 
                   
                     S 
                     = 
                     
                       
                         
                           x 
                           q 
                         
                         ⁡ 
                         
                           ( 
                           
                             
                               q 
                               = 
                               1 
                             
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                             … 
                             ⁢ 
                             
                                 
                             
                             , 
                             Q 
                           
                           ) 
                         
                       
                       = 
                       
                         
                           
                             ( 
                             AB 
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                           ⁢ 
                           
                             ( 
                             
                               
                                 
                                   
                                     π 
                                     A 
                                   
                                 
                               
                               
                                 
                                   
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                         = 
                         
                           
                             
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                               A 
                             
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                     , 
                     
                       
                         
                           π 
                           A 
                         
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                       = 
                       1 
                     
                   
                 
               
               
                 
                   ( 
                   9 
                   ) 
                 
               
             
             
               
                 
                   
                     min 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     P 
                   
                   = 
                   
                     
                       min 
                       
                         
                           π 
                           A 
                         
                         , 
                         
                           π 
                           B 
                         
                       
                     
                     ⁢ 
                     
                       
                         1 
                         2 
                       
                       ⁢ 
                       
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                           S 
                           - 
                           
                             ( 
                             
                               
                                 
                                   π 
                                   A 
                                 
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                               + 
                               
                                 
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                                   B 
                                 
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                           ( 
                           
                             
                               0 
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                                 π 
                                 A 
                               
                             
                             , 
                             
                               
                                 π 
                                 A 
                               
                               ≤ 
                               1 
                             
                             , 
                             
                               
                                 
                                   π 
                                   A 
                                 
                                 + 
                                 
                                   π 
                                   A 
                                 
                               
                               = 
                               1 
                             
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   10 
                   ) 
                 
               
             
           
         
       
     
     [A6.5] 5th Modified Example 
     The detection processing unit  321  in the above embodiment detects the migration speed of the cells of 1000 samples. This may be achieved by making the imaging time long and sampling an arbitrary one point of the dish in the time-lapse image (temporal sampling), or by using arbitrary plural points in the time-lapse image as the samples (spatial sampling). Also, in the detection processing unit  321 , the temporal sampling and the spatial sampling may be combined. 
     [A6.6] 6th Modified Example 
     In the above embodiment, the mixing ratio of each of the plural kinds of cell groups is estimated using the normal distribution and the log normal distribution. However, other probability distribution function such as gamma distribution or beta distribution to be fit to the cell migration speed distribution may be used. 
     As described above, when cell species other than certain cell species to be used is mixed into the objective cell groups, the cell quality evaluation system of this embodiment can perform the quality evaluation of the objective cell groups, e.g., the calculation of purity of certain cell species included in the objective cell groups, by estimating the ratio of the certain cell species using the images. Also, the cell quality evaluation system of this embodiment can evaluate the culture state of the objective cell groups during or after the culture, e.g., cell death of certain cell species or mutation to other cell species during the culture. Therefore, not only the quality control of the objective cell groups and the certain cell species, but also the production control of the cell species can be easily and accurately performed. 
     [B] 2nd Embodiment 
     [B1] Outline of Culture Management System 
     First, an outline of a culture management system  2  according to a second embodiment will be described. 
     The culture management system  2  of this embodiment manages a state of the objective cell groups including plural kinds of cell groups having different attributes in a predetermined culture period using the cell quality evaluation system  1  of the first embodiment. 
     Specifically, the culture management system  2  of this embodiment uses the imaging device  10  and the image processing device  30  in the first embodiment, and manages culture of the objective cell groups including plural kinds of cell groups having different attributes such that the cell group (e.g., fibroblasts) other than the specific cell group to be used (e.g., skeletal myoblasts) is not cultured more than a prescribed degree, e.g., the degree of disabling its use for regenerative medicine, in the objective cell groups in the culture period. 
     Also, the culture management system  2  in this embodiment has such a characteristic point that it executes, in the first embodiment, determination processing of determining whether or not the mixing ratios of the plural kinds of cell groups satisfy the predetermined mixing ratio condition and notification processing of notifying the result of the determination processing to a manager at a predetermined timing or at every predetermined timing in the culture period. 
     It is noted that this embodiment has the same configuration as the first embodiment except for the above-described characteristic point. The same elements are denoted by the same reference numbers, and the description therefore will be omitted. 
     [B2] Configuration of Culture Management System 
     Next, a configuration of the culture management system  2  according to the second embodiment will be described with reference to  FIG. 8 .  FIG. 8  is a system configuration diagram illustrating the configuration of the culture management system  2  of this embodiment. 
     The culture management system  2  of this embodiment includes the imaging device  10  which images the objective cell groups in time series and generates the objective image data, the network  20 , and a culture management device  31  which executes the culture management of the objective cell groups during the culture period. 
     The culture management device  31  has the same functions as the image processing device  30  of the first embodiment, and additionally has functions of determining whether or not the mixing ratios of the plural kinds of cell groups in the objective cell groups satisfy the mixing ratio condition at every predetermined timing in the predetermined culture period and executing a predetermined notification of the determination result to the manager. 
     Namely, as the same functions as the image processing device  30  of the first embodiment, in cooperation with the imaging device  10 , the culture management device  31  analyses the objective image data generated by the imaging device  10  to estimate the mixing ratios of the plural kinds of cell groups, and evaluates the quality of the objective cell groups based on the estimated mixing ratios. 
     Specifically, similarly to the image processing device  30  of the first embodiment, the culture management device  31  executes processing of: 
     (1) acquiring plural objective images of the objective cell groups from the imaging device  10  in time series during a predetermined culture period, 
     (2) detecting a migration speed of each cell in the objective images by analyzing the acquired plural objective images, 
     (3) generating a distribution function or a distribution state of the migration speeds of the imaged objective cell groups based on the detected migration speed of each cell, 
     (4) estimating a mixing ratio of each of the plural kinds of cell groups based on the pre-recorded migration speed information indicating the migration speed of each of the plural kinds of cell groups and the generated distribution function or the generated distribution state, and 
     (5) determining the quality of the objective cell groups as “Passed” when the estimated mixing ratios satisfy the predetermined condition. 
     On the other hand, the culture management device  31  of this embodiment executes the determination processing at predetermined timings (particularly, at every predetermined timing) in the culture period, and executes the predetermined notification to the manager when the mixing ratios of the plural kinds of cell groups satisfy the mixing ratio condition in the determination result. 
     As described above, in case of culturing cells (e.g., skeletal myoblasts) collected from a living body and using the cultured cells for treatments such as regenerative medicine, it is necessary to ensure the quality higher than a certain degree. On the other hand, in the first place, collecting cells from the living body causes an influence to the living body and hence the cells cannot be repeatedly collected. Therefore, it is also necessary to improve the quality of the cells to a usable level to avoid destroying the collected cells for the reason that the quality is not sufficient. 
     Particularly, when cells of a patient himself who receives regenerative medicine (autologous cell) are collected and cultured as the objective cell groups, it is desired to ensure the quality of all the objective cell groups and to use all of them in treatment. 
     Therefore, in this embodiment, the above-described determination processing is executed at every predetermined timing in the culture period to determine the quality of the objective cell groups periodically in the culture period, and the manager is notified whether the quality is maintained or degraded, for example. 
     Particularly, in this embodiment, by executing such notification, it becomes possible to urge the manager to execute predetermined processing, for example, to remove (or kill) unnecessary cell species to prevent further proliferation for the objective cell groups including plural cell species having different doubling times. 
     With the above configuration, in this embodiment, by notifying the condition of the objective cell groups such as the quality of the objective cell groups during culture, it becomes possible to manage the culture of the objective cell groups including plural kinds of cell groups having different attributes, so as to prevent that cell species other than the specific cell species to be used are cultured in the objective cell groups more than a prescribed degree during the culture period and the cell species in the objective cell groups becomes unusable for regenerative medicine, for example. 
     Particularly, in this embodiment, in case of the objective cell groups including cell groups of plural kinds of cell species having different doubling times, such as the objective cell groups collected from a living body (specifically muscle fibers) formed by the skeletal myoblasts and the fibroblasts, it is possible to continue the culture of the cell groups of necessary cell species such as the skeletal myoblasts, while preventing the culture of the cell groups of unnecessary cell species such as the fibroblasts. Therefore, it becomes possible to culture useful objective cell groups including necessary cell species with a high mixing ratio. 
     Accordingly, this embodiment can perform quality control of the objective cell groups during the culture, and make the manager to control the culture of the objective cell groups while performing the quality control, thereby improving production efficiency of the objective cell groups. 
     Next, a configuration of the culture management system  31  of this embodiment will be described with reference to  FIG. 9 .  FIG. 9  is a block diagram illustrating blocks of the culture management device  31  of this embodiment. 
     As shown in  FIG. 9 , the culture management device  31  of this embodiment includes a data recording unit  300 , a communication control unit  310 , a display unit  340 , a display control unit  350 , an operation unit  370  and a management control unit  380 . Further, the culture management device  31  includes an output unit  390  which performs a predetermined notification to the manager, a timer  391 , and a data processing unit  400  which executes quality determination processing based on the generated objective images and culture management. The above blocks are connected with each other via a bus  39  to execute data transfer between the blocks. 
     The data processing unit  400  of this embodiment constitutes a notification control unit of the present invention, and the output unit  390  constitutes an output unit of the present invention. 
     The output unit  390  is a device, such as a speaker, an alarm or a lamp, which notifies the manager of an abnormality of the objective cell groups by sounds or lights during the culture management. 
     Specifically, the output unit  390  notifies the determination result of the determination processing executed at a predetermined timing or at every predetermined timing in the predetermined culture period. 
     For example, when the mixing ratio of each of the plural kinds of cell groups satisfies the mixing ratio condition, the output unit  390  outputs a notification indicating that the condition is satisfied or that the culture can be continued, to the manager by a display, sounds or lights. 
     Also, when the mixing ratio of each of the plural kinds of cell groups does not satisfy the mixing ratio condition, the output unit  390  outputs, to the manager, a notification indicating that the condition is not satisfied or that the culture cannot be continued and certain processing is needed (e.g., processing to suppress culture of unnecessary cell species, such as killing) to the manager by a display, sounds or lights. 
     In this embodiment a predetermined image may be displayed on the display unit  340  to make the display unit  340  function as an output unit for performing notification to the manager. 
     The timer  391  outputs a date and a time (a current time) based on the instruction by the data processing unit  400 , or counts time to make an output when a predetermined timing arrives. 
     Similarly to the first embodiment, in order to execute the migration speed detection processing, the distribution function generation processing, the mixing ratio estimation processing and the pass determination processing, the data processing unit  400  executes applications for the quality determination processing recorded in the ROM/RAM  304  to realize the detection processing unit  321 , the generation processing unit  322 , the estimation processing unit  323  and the determination processing unit  324 . 
     Also, the data processing unit  400  realizes the culture management unit  410  and the notification control unit  420  by executing applications. 
     The culture management unit  410  controls each block at a predetermined timing (or at every predetermined timing) in the culture period, and executes the migration speed detection processing, the distribution function generation processing, the mixing ratio estimation processing and the pass determination processing thereby to acquire the pass determination result. 
     Also, when the culture management unit  410  executes the pass determination processing at every predetermined timing, it changes the mixing ratio condition at every timing. 
     Particularly, the culture management unit  410  uses stricter mixing ratio condition in the determination processing at a timing of a short elapsed time in the culture period, than the mixing ratio condition in the determination processing at a timing of a long elapsed time. 
     For example, similarly to the first embodiment, for the objective cell groups including two cell species of the skeletal myoblasts and the fibroblasts, the culture management unit  410  determines whether or not the ratio to the (whole) objective cell groups satisfies a predetermined condition (e.g., equal to or larger than a constant value) at every predetermined timing such as a culture exchange timing or a subculture timing in the culture period (specifically, primary culture and each subculture is one week) including primary culture and subculture (extended culture). 
     Also, when extended culture is performed N times and the mixing ratio of the necessary cell species (i.e., the skeletal myoblast) for use in regenerative medicine to the whole objective cell groups is equal to or larger than a predetermined ratio (e.g., a minimum ratio usable for regenerative medicine, specifically more than twice the number of cells necessary for transplanting), the culture management unit  410  uses the mixing ratio condition that the mixing ratio of the necessary cell species (e.g., the skeletal myoblast) to the whole objective cell groups is 95% for the (N−2)th extended culture in the culture period (e.g., (N+1) week), and uses the mixing ratio condition that the mixing ratio of the necessary cell species (e.g., the skeletal myoblast) to the whole objective cell groups is 90% for the (N−1)th extended culture. 
     Of course, in the above case, for the Nth extended culture, the culture management unit  410  uses the mixing ratio condition that the mixing ratio of the necessary cell species (e.g., the skeletal myoblast) to the whole objective cell groups is equal to or larger than a predetermined ratio. 
     Then, the culture management unit  410  acquires the result of the determination processing based on the mixing ratio condition. 
     In the above example, when the subculture is executed N times, the mixing ratio condition is changed in the determination processing after the (N−2)th and (N−1)th subcultures. However, the mixing ratio condition may be changed in the primary culture and each of the subcultures, or a constant condition may be used without changing the mixing ratio condition. 
     Also, in this embodiment, the determination processing may be executed for each of the primary culture and the subcultures. Alternatively, the determination processing may be executed during and/or after the primary culture, or during and/or after the subculture. 
     The notification control unit  420  controls the output unit  390  after execution of each determination processing or at a predetermined timing (a timing requested by the manager or after the final determination processing) based on the determination result of each determination processing acquired by the culture management unit  410 , and makes the output unit  390  execute the predetermined notification to the manager. 
     In the first embodiment, the determination processing unit  324  controls the display control unit  350  to display the determination result on the display unit  340 . The notification control unit  420  of this embodiment may control the display control unit  350  to display an image for executing a predetermined notification on the display unit  340 . 
     [B4] Operation of Culture Management System 
     Next, a description will be given of an operation of the culture management processing executed by the culture management device  31  of the culture management system  2  according to this embodiment with reference to  FIG. 10 .  FIG. 10  is a flowchart illustrating the operation of the culture management processing executed by the culture management device  31  of the culture management system  2  according to this embodiment. 
     In this operation, it is supposed that the determination processing is executed at every timing in the culture period, and a predetermined notification is made to the manager as a warning when the mixing ratio condition in each determination processing is not satisfied. 
     Also, in this operation, it is supposed that the culture of the objective cell groups is performed in a predetermined dish set in an incubator not shown, and it is supposed that the objective images of the objective cell groups are generated by the imaging device  10  at predetermined timings in the culture, acquired in time series and recorded in the data recording unit  300 . 
     Also, in this operation, the migration speed information of the plural kinds of cell groups having different attributes included in the objective cell groups of the objective image are recorded in the data recording unit  300  in advance. 
     Further, in this operation, the migration speed detection processing is executed by an automatic tracking. 
     First, when the culture management unit  410  detects the start of the culture period based on the operation by the manager (step S 201 ), it makes the timer  391  start counting time (step S 202 ). 
     Next, the culture management unit  410  makes the timer  391  determine whether or not the current time is the timing to execute the determination processing (step S 203 ). If the timer determines that the current time is the timing to execute the determination processing, the processing moves to step S 212 . If the timer  391  determines that the current time is not the timing to execute the determination processing, the culture management unit  410  waits for a predetermined time period (step S 204 ), and returns to step S 203 . 
     Next, if the timer  391  determines that the current time is the timing to execute the determination processing, the culture management unit  410  selects the objective images of the objective cell groups (i.e., a specific objective cell groups) on the dish based on the operation to the operation unit  370 , and acquires plural time-series objective images of the certain objective cell groups thus selected (step S 212 ). 
     Next, the detection processing unit  321  identifies tracking of each cell included in the imaged objective cell groups and detects the migration speed of each cell (step S 213 ). Specifically, when the objective cell groups include 100 cells (or it is supposed that the objective cell groups include about 100 cells), the detection processing unit  321  acquires the objective image of about 10 frames. 
     Next, the generation processing unit  322  calculates an average and a variance of the migration speed of each detected cell, and calculates the distribution function (the mixed distribution function) of the log normal deviation based on the average and the variance thus calculated (step S 214 ). 
     Next, the estimation processing unit  323  reads out the migration speed information (the average and the variation) of the corresponding cell group from the reference data recording unit  303  (step S 215 ), and calculates the variable “π” of the mixing ratio satisfying the equations (3) and (4) according to a predetermined algorithm based on the read-out migration speed information and the calculated mixed distribution function, thereby to identify the ratio of the mixed distribution function (step S 216 ). 
     Next, the determination processing unit  324  executes the pass determination processing which determines whether or not the ratio of the objective cell to the objective cell groups satisfies the predetermined condition (equal to or larger than a constant value) (step S 217 ). Specifically, the determination processing unit  324  determines whether or not the predetermined condition is satisfied. At that time, the determination processing unit  324  determines “Passed” when the ratio of the objective cell to the objective cell groups satisfies the predetermined condition, and determines “Failed” when the ratio does not satisfy the predetermined condition. 
     Next, in cooperation with the display control unit  350 , the notification control unit  420  makes the display unit  340  and the output unit  390  output the determination result, i.e., the result of Passed or Failed, to be notified to the manager (step S 218 ). 
     When the mixing ratio of the objective cell to the objective cell groups does not satisfy the predetermined condition, the culture management unit  410  urges to execute predetermined processing, e.g., processing to kill the cell species other than the objective cell. 
     When the mixing ratio of the objective cell with respect to the objective cell groups does not satisfy the predetermined condition, the culture management unit  410  may stop the culture of the objective cell and forcedly terminate this operation. 
     Finally, the culture management unit  410  determines whether or not next determination processing exists (step S 219 ). When it is determined that the next determination processing exists, the culture management unit  410  moves to step S 203 . When it is determined that the next determination processing does not exist (i.e., the processing in the previous step S 217  is the final determination processing), the culture management unit  410  ends this operation. 
     [C] 3rd Embodiment 
     [C1] Outline of Cell Group Production Method 
     Next, description will be given of an outline and an operation principle of a cell group production method according to the third embodiment. 
     The cell group production method of the this embodiment produces at least specific kinds of cell groups by controlling the culture in a predetermined culture period for the objective cell groups collected from a living body and including plural kinds of cell groups having different attributes. 
     The cell group production method of the this embodiment uses the culture management system  2  including the culture management device  31  having a culture inhibition function described later. When the objective cell groups are usable after the predetermined culture period, the cell group production method executes process for executing predetermined processing, such as cryopreservation and standard test, for using the objective cell groups to the living body. 
     Specifically, the cell group production method executes: 
     (A) inspection processing including at least the determination processing of determining whether or not the mixing ratio of each of the plural kinds of cell groups included in the objective cell groups to the cultured objective cell groups satisfies the predetermined mixing ratio condition at predetermined timings in the predetermined period by using the culture management system  2 , and 
     (B) inhibition processing of inhibiting culture of unnecessary cell species in the objective cell groups or preparation processing for executing the inhibition processing, when the determination processing determines that the mixing ratio of each of the plural kinds of cell groups does not satisfy the mixing ratio condition. 
     In the cell group production method of the this embodiment, as the inspection processing of (A), similarly to the second embodiment, the culture management system  2 : 
     (A1) acquires a plurality of objective image data of the objective cell groups in time series at a predetermined timing (or at every predetermined timing), 
     (A2) detects the migration speed of each cell imaged in the objective image by analyzing the plurality of acquired objective image data, 
     (A3) generates the distribution function or the distribution state of the migration speeds of the imaged objective cell groups based on the detected migration speed of each cell, and 
     (A4) estimates the mixing ratio of each of the plural kinds of cell groups included in the objective cell groups to execute the determination processing based on the mixing ratio condition, on the basis of the migration speed information recorded in the database and including information of the migration speed of each of the plural kinds of cell groups, and the generated distribution function or the generated distribution state. 
     Also, in the cell group production method of the this embodiment, as the processing (B), the culture management system  2  executes (B1) the inhibition processing of inhibiting the culture of unnecessary cell species in the objective cell groups so as to stop the proliferation of the unnecessary cell species (including killing), or (B2) the preparation processing for executing the inhibition processing such as urging the manager to execute the inhibition processing. 
     Then, in the cell group production method of the this embodiment, when the mixing ratio of each of the plural kinds of cell groups satisfies the mixing ratio condition (e.g., the ratio of the cell group of necessary cell species to the objective cell groups is equal to or larger than a predetermined ratio) in the determination processing at the ending time of the culture period, it is determined that the objective cell groups are usable and pass the final inspection processing. 
     On the other hand, in the cell group production method of the this embodiment, when it is determined in the final inspection processing that the objective cell groups are usable, cryopreservation is performed before the use timing. At the use timing, a final use determination is executed by the specification testing. When the objective cell groups pass the specification testing, it is processed to a use form such as a cell sheet. 
     In the this embodiment, with the above configuration, when the cell species other than specific cell species (e.g., skeletal myoblast) to be used is mixed in the objective cell groups for example, the culture of the objective cell groups during the culture (i.e., the mixing ratio of the plural kinds of cell species in the objective cell groups) may be controlled. 
     Namely, in the this embodiment, when the mixing ratio of the unnecessary cell species in the objective cell groups becomes larger than a specified ratio, it does not pass the inspection processing, and hence it becomes possible to stop the use of the objective cell groups having low quality. Additionally, since the mixing ratio can be controlled by inhibiting the culture of the unnecessary cell species in the objective cell groups after the inspection processing, it becomes possible to make the objective cell groups finally pass the inspection processing. 
     Accordingly, in the this embodiment, it is possible to culture and produce the objective cell groups of proper quality and to improve production efficiency of the objective cell groups. 
     In the third embodiment, it is supposed that the culture management device  31  has a culture inhibition function. 
     In the cell group production method of the this embodiment, the culture management device  31  of the culture management system  2  may have the culture function, or the culture management system  2  may have the culture device (not shown) separately from the culture management device  31 . 
     For example, the culture device (the culture management device  31 ) maintains its inside atmosphere to satisfy a predetermined environmental condition, and includes a thermostatic chamber capable of housing a plurality of culture containers for culturing cells and an imaging unit which generates the images of the cultured cells as described in the first embodiment. 
     [C2] Principle of Cell Group Production Method 
     Next, the principle of the cell group production method according to the third embodiment will be described with reference to  FIGS. 11, 12A and 12B . 
       FIG. 11  is a diagram for explaining the principle of the cell group production method according to this embodiment.  FIGS. 12A and 12B  are diagrams illustrating identifying the fibroblasts from the image of the objective cell groups in which the fibroblasts and the skeletal myoblasts are mixed in the third embodiment.  FIG. 12A  is a diagram illustrating a phase contrast image of the objective cell groups of a mouse imaged by a phase contrast microscope, and  FIG. 12B  is a diagram illustrating an image of the objective cell groups of  FIG. 12A  in which the fibroblasts are fluorescently dyed. 
     First, the objective cell groups collected from a living body is put in a dish in which a specified culture medium is placed, and the culture (the primary culture) is started. The culture is performed a predetermined period (e.g., 7 days) (the first phase in  FIG. 11 ). 
     At that time, the culture management device  31  starts the culture management based on the operation by the manager, and manages the dish in which the culture is started in an incubator. During the primary culture, the culture medium is exchanged at a predetermined timing (e.g., 4th day from the start of the primary culture). 
     Next, when the primary culture ends, the subculture is performed N times (N: natural number) in which the objective cell is separated and cultured (the second phase in  FIG. 11 ). 
     At that time, the culture management device  31  executes the culture management based on the operation of the manager, and manages the dishes in which the objective cell groups are cultured in the incubator similarly to the primary culture. Similarly to the primary culture, the culture medium is exchanged at a predetermined timing (e.g., 4th day from the start of the primary culture) in each of the subcultures. 
     On the other hand, during the primary culture and subcultures (specifically, during the imaging period), the imaging device  10  takes still pictures of the objective cell groups placed in the dish at every predetermined timing (e.g., every 6 or 12 minutes) by the imaging function based on the control by the culture management device  31  or the operation by the manager, and generates the objective image data as the time-lapse images. The generated objective image data is recorded in the culture management device  31  together with the time information indicating the imaging time (the first and second phases in  FIG. 11 ). 
     Also, the culture management device  31  executes the culture management processing (i.e., the determination processing) according to the second embodiment after the primary culture, each of the subcultures or the subcultures of a predetermined number of times. Then, the culture management device  31  calculates the mixing ratio of the objective cell to the objective cell groups in each dish, and executes the pass determination of the quality of the objective cell image of each dish based on the predetermined mixing ratio condition (i.e., the pass determination in the inspection processing) (the first and second phases in  FIG. 11 ). 
     The mixing ratio condition may be changed at the time of the determination processing of each culture (the passing standard may be set higher for the initial cultures). The mixing condition may be kept unchanged at the time of the determination processing of each culture, or a mixing ratio condition different from other determination processing may be used only at the time of the determination processing of a certain culture. 
     Then, for the objective cell groups in the dish determined as Failed, the culture management device  31  executes processing (hereinafter referred to as “inhibition processing”) for inhibiting culture of the unnecessary cells such as removing (or killing) the cell groups of unnecessary cell species, or executes the preparation processing for making the culture management device  31  to execute the inhibition processing (the first and second phases in  FIG. 11 ). 
     Specifically, as the inhibition processing, the culture management device  31 : 
     (1) identifies the unnecessary cell group (specifically, the fibroblast) and the necessary cell group (specifically, the skeletal myoblast) from an entire area of the image by a pattern matching (color or shape) or based on the migration speed, and 
     (2) removes the cell group of the unnecessary cell species contactlessly by irradiating a near-infrared laser on the cell group of the unnecessary cell species. 
     For example, when the pattern matching is used in the processing (1), the culture management device  31  compares each cell with the data stored in advance and indicating the form of the fibroblast, and identifies the cells clearly different from the skeletal myoblast and similar to the fibroblast (e.g., the cell having a coincidence ratio equal to or larger than 90% with the data of the fibroblast). 
     For example, as shown in  FIGS. 12A and 12B , in the objective cell groups during culture in which the skeletal myoblasts and the fibroblasts are mixed, it is shown that the form of the fibroblast is remarkably different from the skeletal myoblast, in theory. Therefore, it is normally possible to identify the fibroblast by the pattern matching. 
     The fibroblast sometimes has a form similar to the skeletal myoblast, or the skeletal myoblast sometimes has a form similar to the fibroblast. However, in the present invention, since the objective cell groups are determined to be usable when the mixing ratio is larger than a constant ratio, the cells hard to discriminate as described above are not identified as the fibroblast, and only the cells surely identified as the fibroblast are killed. 
       FIG. 12A  is a phase contrast image of the objective cell groups of a mouse taken by the phase contrast microscope, and the image of the objective cell groups during coculture of C2C12 (mouse myoblast) and Swiss3T3 (mouse fibroblast) in which the skeletal myoblasts and the fibroblasts are mixed.  FIG. 12B  is a figure in which the fibroblasts are fluorescently dyed in  FIG. 12A . 
     Then, the culture management device  31  irradiates a near-infrared laser on the cells identified as the fibroblast to be killed. 
     On the other hand, as the preparation processing, the culture management device  31  executes the notification to the manager urging the execution of the inhibition processing or the notification of the dish information therefor to the manager. 
     It is noted that Japanese patent application Laid-Open under No. 2016-154482 discloses a technique of identifying an area in which the objective cells are differentiated in the inhibition processing by an image analysis, and therefore its description will be omitted. 
     Next, when the primary culture and the subcultures of the objective cell groups end, the objective cell groups are cryopreserved until they are used (the third phase in  FIG. 11 ), and the specification testing is executed when the objective cell groups are used (the fourth phase in  FIG. 11 ). 
     Finally, the objective cell groups which have passed the specification testing are processed to the use form, e.g., processed to a sheet (the fifth phase in  FIG. 11 ), and are used in regenerative medicine. 
     Particularly, the specification testing is a test for confirming whether or not the objective cell groups are suitable for the use in regenerative medicine, and is a test appropriately determined in accordance with the situation and/or contents of using the objective cell groups. 
     BRIEF DESCRIPTION OF REFERENCE NUMBERS 
     
         
         
           
               1  Cell quality evaluation system 
               2  Culture management system 
               10  Imaging device 
               20  Network 
               30  Image processing device 
               31  Culture management device 
               300  Data recording unit 
               301  Application recording unit 
               302  Image data recording unit 
               303  Reference data recording unit 
               310  Communication control unit 
               320 ,  400  Data processing unit 
               321  Detection processing unit 
               322  Generation processing unit 
               323  Estimation processing unit 
               324  Determination processing unit 
               340  Display unit 
               350  Display control unit 
               370  Operation unit 
               380  Management control unit 
               390  Output unit 
               391  Timer 
               410  Culture management unit 
               420  Notification control unit