Patent Description:
A urinary sediment examination is one example of a method in which material components contained in a sample solution are measured. A urinary sediment examination is an examination performed to investigate the amount and types of material components present in urine. In a urinary sediment examination, the content of each type of material component present in urine is investigated. In a urinary sediment examination, evaluation is performed based on measurements taken in a microscopic examination (standard method) using counts of material components present in a whole visual field (the range visible at any one time when viewed through a microscope). As an example of determination criteria, <NUM> to <NUM> red blood cells and <NUM> to <NUM> white blood cells in a whole visual field are determined to be normal. The whole visual field employed in evaluation varies according to the type of material components, and strong magnification (<NUM>×) or weak magnification (<NUM>×) may be employed. It is recommended to use <NUM> visual fields using an eyepiece lens having a magnification of <NUM>×.

Progress is being made to automate such urinary sediment examinations (also referred to hereafter as "urinary material component examinations"). Broadly speaking, three methods are employed therefor: (<NUM>) observation and image capture of static samples using a microscope, (<NUM>) image capture using a flow method, and (<NUM>) scattergram analysis using a flow method.

For example, a method to display ultrafine particles is described in Japanese Patent Application Publication (<CIT>. In this method a dilute biological liquid test sample containing ultrafine particles in a static state is not subjected to a physical concentration method such as centrifugal separation or the like, and instead the ultrafine particles are electronically concentrated. This method includes a step of distributing the liquid test sample over a wide area such that there is substantially no overlap between particles, and a step of forming plural still optical images of the liquid test sample to cover a plane of this area such that each of the still optical images represents a different location within this area. This method further includes a step of converting each of the still optical images into respective electronic images, and a step of combining images representing different particles from these electronic images to form a single composite electronic image. This method also includes a step of removing unnecessary image portions from the composite electronic image, or of processing the composite electronic image to extract useful image portions therefrom, and a step of displaying the processed composite electronic image as an image of the electronically concentrated ultrafine particles. Note that the method described in <CIT> is technology relating to observation of a static sample using a microscope, as in method (<NUM>) above.

In image capture using a flow method as in method (<NUM>) above, images are captured of a test sample flowing through a flow cell, and material component images extracted from the captured images are categorized by material component type and examined. Image capture using a flow method does not allow the observation visual field to be moved or the magnification ratio to be changed while observing a sample, as is possible when observing a static sample through a microscope. Moreover, since the material component images extracted from the captured images are employed in analysis, the captured images themselves are not retained. Since an image of a static sample observed through a microscope includes various types of material components, there is accordingly a desire to view an image including plural material components even when image capture is performed using a flow method in order to enable the distribution of the material components and states of the material components to be observed at a glance.

Further background art is provided in <CIT>, <CIT> and <CIT>.

<CIT> discloses a method for imaging a plurality of particles using a particle analysis system configured for combined viscosity and geometric hydrofocusing, the particles included in a blood fluid sample having a sample fluid viscosity, the method comprising: flowing a sheath fluid along a flowpath of a flowcell, the sheath fluid having a sheath fluid viscosity that differs from the sample fluid viscosity by a viscosity difference in a predetermined viscosity difference range; injecting the blood fluid sample into the flowing sheath fluid within the flowcell so as to provide a sample fluid stream enveloped by the sheath fluid; flowing the sample fluid stream and the sheath fluid through a reduction in flowpath size toward an imaging site, such that a viscosity hydrofocusing effect induced by an interaction between the sheath fluid and the sample fluid stream associated with the viscosity difference, in combination with a geometric hydrofocusing effect induced by an interaction between the sheath fluid and the sample fluid stream associated with the reduction in flowpath size, is effective to provide a target imaging state in at least some of the plurality of particles at the imaging site while a viscosity agent in the sheath fluid retains viability of cells in the sample fluid stream leaving structure and content of the cells intact when the cells extend from the sample fluid stream into the flowing sheath fluid; and imaging the plurality of particles at the imaging site.

<CIT> discloses a particle image analyzing method comprising: detecting particles flowing through a flow cell; determining from a particle detection signal obtained whether desired particles are to be imaged, and then acquiring images of the desired particles; storing an acquired overall image of a sample into an overall-image memory; extracting particle components contained in the sample, and the number of the particles, from the acquired overall image of the sample; analyzing the extracted particle components in accordance with feature parameters, then after classifying the particle components according to the kind of component, computing respective concentrations of the classified components, and storing the classified components with the computed concentrations into a cropped image memory; displaying on display means the overall image stored in the overall-image memory; and in accordance with to-be-added or to-be-changed particle component information entered from operating means, conducting modifications and concentration-modifying computations upon the components stored in the cropped image memory.

<CIT> discloses a urine analysis system comprising: a testing apparatus that measures particles included in a urine sample according to a flow cytometry method; an image capturing apparatus that captures images of particles in the urine sample to acquire cell images; and a management apparatus that receives a measurement result obtained by the testing apparatus and the cell images acquired by the image capturing apparatus. The management apparatus generates an image capturing order for the urine sample based on the measurement result obtained by the testing apparatus. The image capturing apparatus executes the image capturing processing of the particles in the urine sample for which the image capturing order has been generated by the management apparatus, and transmits the acquired cell images to the management apparatus.

In consideration of the above circumstances, the present invention is as defined in the appended claims, and provides an information processing device, an information processing method, a measurement system, and a non-transitory storage medium capable of obtaining an image including plural material components such as that observed through a microscope, even in cases in which image capture is performed using a flow method.

Exemplary embodiments of the present invention will be described in detail based on the following figures, wherein:.

Detailed explanation follows regarding an example of an exemplary embodiment of the present disclosure, with reference to the drawings.

<FIG> is a perspective view illustrating part of a configuration of a measurement system <NUM>.

As illustrated in <FIG>, the measurement system <NUM> according to the present exemplary embodiment includes a flow cell <NUM>, casing <NUM>, a camera <NUM>, and a light source <NUM>. Note that the arrow UP in <FIG> indicates upward in a vertical direction of the measurement system <NUM>.

The flow cell <NUM> is, for example, applicable to urinary material component examinations (urinary sediment examinations) in which a urine sample serving as an example of a sample fluid is introduced together with a sheath fluid in order to capture images of material components in the urine sample using the camera <NUM> and to perform various analyses based on the shapes etc. of the material components in the captured image. The camera <NUM> is an example of an imaging section. Although explanation follows regarding an example in which a urine sample is not in a concentrated state, a concentrated sample may also be employed therefor. Plural types of material components (solid components) are contained in the urine sample. Examples of types of material components include red blood cells, white blood cells, epithelial cells, casts, bacteria, and the like. Note that although a case is described in which a urine sample serving as an example of a sample fluid is used to perform a urinary material component examination, blood, cells, bodily fluids, or the like may also be employed for material component examination.

The measurement system <NUM> includes the casing <NUM> to place the flow cell <NUM> in. A recess 72A for inserting the flow cell <NUM> into is formed in the casing <NUM>. A position of the casing <NUM> that includes where the recess 72A is provided is formed by a transparent member (for example glass). The camera <NUM> is provided inside the casing <NUM> at a position facing toward the flow cell <NUM>. The light source <NUM> is provided at the upper side of the casing <NUM>, at a position facing toward the camera <NUM> across the flow cell <NUM>. The camera <NUM> is disposed at a position that enables the sample fluid flowing through the flow cell <NUM> to be imaged.

The measurement system <NUM> includes a first supply device <NUM> to supply the sample fluid into a sample intake port <NUM> of a sample flow path (not illustrated in the drawings) in the flow cell <NUM>. The first supply device <NUM> includes a supply tube <NUM> having one end connected to the sample intake port <NUM>, a pump <NUM> provided partway along the supply tube <NUM>, and a sample storage section <NUM> for storing the sample fluid in that is connected to the other end of the supply tube <NUM>.

The measurement system <NUM> includes a second supply device <NUM> to supply the sheath fluid into a sheath intake port <NUM> of a sheath flow path (not illustrated in the drawings) in the flow cell <NUM>. The second supply device <NUM> includes a supply tube <NUM> having one end connected to the sheath intake port <NUM>, a pump <NUM> provided partway along the supply tube <NUM>, and a tank <NUM> for storing the sheath fluid in that is connected to the other end of the supply tube <NUM>.

A discharge port <NUM> is also provided to the flow cell <NUM> between the sample intake port <NUM> and the sheath intake port <NUM>. One end of a discharge tube (not illustrated in the drawings) is connected to the sheath intake port <NUM>, and the other end of the discharge tube is connected to a waste tank (not illustrated in the drawings). The flow cell <NUM> includes a merging section (not illustrated in the drawings) to merge the sample introduced through the sample intake port <NUM> with the sheath fluid introduced through the sheath intake port <NUM>, and the merged fluid flows through a flow path. A tapered section is formed in the flow path where the height of the flow path gradually decreases. The sample therefore does not disperse in the sheath fluid even after the sample has merged with the sheath fluid, and instead the flow is localized to a flattened shape. The material components in the localized sample fluid are imaged by the camera <NUM>.

<FIG> is a schematic diagram illustrating an example of a configuration of the measurement system <NUM>.

As illustrated in <FIG>, the measurement system <NUM> includes an information processing device <NUM>. Note that the arrow UP in <FIG> indicates upward in the vertical direction of the measurement system <NUM>, similarly to in <FIG>.

The information processing device <NUM> includes the functionality of a control device to control the respective operations of the camera <NUM>, a light source actuation section <NUM> electrically connected to the light source <NUM>, the pump <NUM>, and the pump <NUM>. The information processing device <NUM> applies a pulse signal to the light source actuation section <NUM> so as to cause the light source <NUM> to emit light at a predetermined interval. The information processing device <NUM> drives the pump <NUM> to control the flow rate of the sample fluid, and drives the pump <NUM> to control the flow rate of the sheath fluid.

<FIG> is a block diagram illustrating an example of an electrical configuration of the information processing device <NUM>.

As illustrated in <FIG>, the information processing device <NUM> includes a control section <NUM>, a storage section <NUM>, a display section <NUM>, an operation section <NUM>, and a communication section <NUM>.

For example, a generic computer such as a personal computer (PC) is employed as the information processing device <NUM>. Note that a mobile computer such as a smartphone, tablet, or the like may also be employed as the information processing device <NUM>. The information processing device <NUM> may also be split between plural units. For example, the information processing device <NUM> may be configured so as to include a unit for controlling a measurement system composed of the camera <NUM>, the light source <NUM>, the pump <NUM>, the pump <NUM> etc., and to include a unit to perform processing and analysis on images captured by the camera <NUM>. The information processing device <NUM> may be an external device connected to the measurement system <NUM>.

The control section <NUM> includes a central processing unit (CPU) 12A, read only memory (ROM) 12B, random access memory (RAM) 12C, and an input/output interface (I/O) 12D. These sections are connected together through a bus.

Each of the functional sections including the storage section <NUM>, the display section <NUM>, the operation section <NUM>, and the communication section <NUM> are connected to the I/O 12D. These functional sections are capable of communicating with the CPU 12A through the I/O 12D.

The control section <NUM> may be configured as a sub-control section to control the operation of parts of the information processing device <NUM>, or may be configured as part of a main control section to control overall operation of the information processing device <NUM>. For example, large scale integrated (LSI) circuits or integrated circuit (IC) chip sets are employed for some or all of the respective blocks of the control section <NUM>. A separate circuit may be employed for each block, or circuits integrating together some or all of the blocks may be employed. The respective blocks may be provided so as to be integrated together, or some of the blocks may be provided separately. Alternatively, parts of the respective blocks may be provided separately. Integration of the control section <NUM> is not limited to integration employing LSIs, and dedicated circuits or generic processors may also be employed therefor.

Examples of the storage section <NUM> include a hard disk drive (HDD), a solid state drive (SSD), a flash memory, or the like. An image processing program 14A to perform processing for all-component image generation is stored in the storage section <NUM>. Note that the image processing program 14A may alternatively be stored in the ROM 12B. The storage section <NUM> is also stored with a material component image set 14B for use in the all-component image generation processing. Note that the storage section <NUM> may have external memory attached that is expanded later.

The image processing program 14A may, for example, be pre-installed in the information processing device <NUM>. The image processing program 14A may be stored on a non-volatile storage medium, or may be implemented by being distributed via a network and installed or uploaded to the information processing device <NUM> as appropriate. Note that examples of non-volatile storage media include compact disc read only memory (CD-ROM), a magneto-optical disc, an HDD, digital versatile disc read only memory (DVD-ROM), flash memory, a memory card, and so on.

For example, a liquid crystal display (LCD) or an organic electro luminescence (EL) display may be employed as the display section <NUM>. The display section <NUM> may include an integral touch panel. A device for operation input, such a keyboard or a mouse, may be provided to the operation section <NUM>. The display section <NUM> and the operation section <NUM> receive various instructions from a user of the information processing device <NUM>. The display section <NUM> displays various information, such as the result of processing executed in response to an instruction received from the user and notifications relating to processing.

The communication section <NUM> is connected to a network such as the internet, a local area network (LAN), or a wide area network (WAN), and is capable of communicating over the network with external devices such as an image forming device or another PC.

When performing image capture using a flow method employing the flow cell <NUM> as described above, images of the sample fluid flowing through the flow cell <NUM> are captured, and anything identified as a material component is extracted from the captured images. These extracted images of material components are categorized by material component type, and examined. This accordingly means that an all-component image resembling an observation through a microscope is not obtainable. There is accordingly a desire to view an all-component image including plural material components even when image capture is performed using a flow method, in order to enable the distribution of the material components and states of the plural material components to be observed at a glance in such an all-component image.

The CPU 12A of the information processing device <NUM> functions as the respective sections illustrated in <FIG> by writing the image processing program 14A stored in the storage section <NUM> into the RAM 12C and executing the image processing program 14A.

<FIG> is a block diagram illustrating an example of a functional configuration of the information processing device <NUM>.

As illustrated in <FIG>, the CPU 12A of the information processing device <NUM> functions as a categorizing section <NUM>, a count derivation section <NUM>, a generation section <NUM>, a display control section <NUM>, and a setting section <NUM>. Note that the display control section <NUM> is an example of a control section.

The categorizing section <NUM> takes plural images (for example <NUM> or <NUM> images) obtained by the camera <NUM> imaging the sample fluid flowing through the flow cell <NUM>, and from the plural images, extracts material component images of the plural types of material component contained in a sample fluid. The categorizing section <NUM> then categorizes the extracted material component images into predetermined categories (for example by the type, size, or shape of the material components, or by the presence or absence of a nucleus therein). The material component images categorized into the predetermined categories by the categorizing section <NUM> are stored in the storage section <NUM> as a material component image set 14B for each sample. Note that various known methods may be employed as the method for identifying material components in the images, such as a method employing machine learning or a method employing pattern matching. The material component image set 14B is also referred to as the material component images 14B when referring to individual material component images within the material component image set 14B.

The count derivation section <NUM> derives counts by category for material components contained in the sample fluid, and counts of material components present per standard visual field, based on the number of material component images categorized into each category by the categorizing section <NUM>. Specifically, for each of the predetermined categories, the count derivation section <NUM> uses a calibration curve to convert the number of material component images acquired from a single sample measurement into counts per unit liquid volume of the sample fluid. Counts per standard visual field are then derived from the thus converted counts per unit liquid volume by using a predetermined correction coefficient for each visual field. Note that reference to calibration curves also encompasses functions, and data expressing correlations such as conversion coefficients, conversion tables, etc..

<FIG> is a graph illustrating an example of a calibration curve as employed for each material component.

In <FIG>, the horizontal axis represents the count of material components actually identified in a single sample measurement, and the vertical axis represents the count of material components per unit liquid volume of the sample fluid. Note that a calibration curve for red blood cells is illustrated as a representative example.

The camera <NUM> is not actually capable of imaging all of the sample fluid when the sample fluid flows through the flow cell <NUM>. A sample amount imageable by the camera <NUM> is accordingly ascertained. The imageable sample amount is determined using a control liquid containing standard particles. For example, if <NUM> standard particles are captured in plural images imaged by the camera <NUM> when <NUM>µl of a control liquid containing <NUM> standard particles per µl flows through the flow cell <NUM>, then the sample amount imageable by this measurement system is determined to be <NUM>µl, or in other words a sample amount corresponding to <NUM>% of the sample amount that flowed.

The differing size, shape, composition, outline definition, and the like for each type of material component results in differences in the ease (rate) of flowing through an imaging area of the camera <NUM>, the ease with which the camera <NUM> can be focused thereon, and the ease with which the material components can be identified in the captured images. There is accordingly a difference by category in the rate with which the material components are captured as extracted images.

In consideration of the above points, a calibration curve expressed by a function y = f(x) is created so as to reflect the imageable sample amount and the capture rate for each category. The calibration curve illustrated in <FIG> is a graph representing a relationship between red blood cell counts (measured values) actually identified in plural images captured by the camera <NUM>, and red blood cell counts contained per unit liquid volume. The graph expressing this calibration curve is created based on actual measurement results obtained and on accurate count data for the corresponding material component as measured using another method. In addition to red blood cells, such calibration curves are also created for the other categories, namely white blood cells, epithelial cells, casts, bacteria, and the like. Note that data of the calibration curves created is stored in advance in the storage section <NUM> for reference by the count derivation section <NUM>. The calibration curves are used to acquire counts of material components contained per unit liquid volume (for example per <NUM>µl) of the sample from the number of material component images.

Predetermined correction coefficients for respective visual fields are then used to derive the counts per standard visual field for material components of each category from the counts per unit liquid volume obtained for each category. Here, a "visual field" refers to the range that can be seen at any one time through a microscope using, for example, a high power field (HPF: strong magnification ratio equivalent to <NUM>×) or a low power field (LPF: weak magnification ratio equivalent to <NUM>×) for microscopic examination (standard method). The correction coefficient is decided based on the surface area of the standard visual field, as well as the placement of the sample at the imaging location, the concentration multiplier, and the urine sample volume. α is the correction coefficient for HPF, and β is the correction coefficient for LPF. The correction coefficient α is set based on the relationship between the imaging range captured by the camera <NUM> and that for an HPF, and the correction coefficient β is set based on the relationship between the imaging range captured by the camera <NUM> and that for an LPF. The correction coefficients α, β are stored in advance in the storage section <NUM>. Note that Nα and Nβ are computed using the following Equations (<NUM>) and (<NUM>), wherein Nα is a count for an HPF, Nβ is a count for an LPF, and Nu is a count per unit liquid volume. Note that Nu is different for each category. <MAT> <MAT>.

Explanation follows regarding specific examples in which HPF and LPF counts are derived for red blood cells, white blood cells, and bacteria. For example, if the actual red blood cell count measured is <NUM>, then the calibration curve for red blood cells is employed to convert this count into a count of <NUM>/µl. Then Equation (<NUM>) and Equation (<NUM>) are used to derive counts of (α × <NUM>) / HPF and (β × <NUM>) / LPF. Moreover, if the actual white blood cell count measured is <NUM>, the calibration curve for white blood cells is employed to convert this count into a count of <NUM>/µl, and then Equation (<NUM>) and Equation (<NUM>) are used to derive counts of (α × <NUM>) / HPF and (β × <NUM>) / LPF. Moreover, if the actual bacteria count measured is <NUM>, the calibration curve for bacteria is employed to convert to this count into a count of <NUM>/µl, and then Equation (<NUM>) and Equation (<NUM>) are used to derive counts of (α × <NUM>) / HPF and (β × <NUM>) / LPF. Note that the standard visual field may also employ a visual field with a magnification ratio other than that of HPF or LPF, and a user may set a desired standard visual field size.

Next, the generation section <NUM> generates a single all-component image in which plural material component images corresponding to the counts for each category per standard visual field, as derived by the count derivation section <NUM>, are randomly arranged in an area corresponding to the standard visual field. When this is performed, the plural material component images are preferably arranged such that they do not overlap each other. The background color often differs between each of these plural material component images, and this could result in an unnatural looking all-component image. In order to reduce any such unnaturalness in the all-component image the plural material component images are preferably adjusted to a uniform color tone. This color tone may, for example, be set as any one out of an average value, a maximum value, or a minimum value of pixel values representing the background colors of the plural material component images. Moreover, images in which the material component images are in focus are preferably employed therefor. The all-component image is a pseudo image generated by arranging material component images, which have been obtained by categorizing the images extracted from the captured images by material component, according to the respective quantities thereof contained in the sample fluid. For example, in a case in which the standard visual field is set to HPF (equivalent to <NUM>×), the generated all-component image is an image equivalent to an image with a <NUM>× magnification ratio as observed in a microscopic examination (standard method).

Next, as an example, the display control section <NUM> controls so as to display the all-component image generated by the generation section <NUM>, as illustrated in <FIG>.

<FIG> is a face-on view illustrating an example of an all-component image screen <NUM>.

As illustrated in <FIG>, the all-component image screen <NUM> displays the all-component image in which the material component images 14B are arranged according to the counts per category, as described above. The all-component image screen <NUM> is displayed on the display section <NUM>. Although an example is described above in which the all-component image is created for the counts contained in a standard visual field, the all-component image may be created for a desired setting for the liquid volume and image size based on the respective counts contained per unit liquid volume of the sample, or the all-component image may be created for a unit liquid volume based thereon. Moreover, a display mode may be selected or changed by a user. In cases in which an all-component image is created for a unit liquid volume, the user is able to set or change the unit liquid volume to the desired unit liquid volume.

The display control section <NUM> may perform control so as to selectively display only specific material component images contained in the all-component image. Such control of display may, for example, be performed by selecting an item button corresponding to the specific material component image from an item button array <NUM>, illustrated in <FIG>.

<FIG> is a diagram illustrating an example of the item button array <NUM>.

As illustrated in <FIG>, the item button array <NUM> is displayed on the display section <NUM> together with the all-component image of the all-component image screen <NUM>. The item button array <NUM> includes item buttons 51A, 51B, 51C, etc. corresponding to each of the material components.

As an example, the item button array <NUM> illustrated in <FIG> includes an item button 51A corresponding to red blood cells, an item button 51B corresponding to white blood cells, an item button 51C corresponding to casts, and so on. For example, when the item button 51A has been selected, only images of red blood cells are selectively displayed on the all-component image screen <NUM> illustrated in <FIG>, and the other material components are not displayed. Similarly, when the item button 51B has been selected, images of only white blood cells are selectively displayed on the all-component image screen <NUM> illustrated in <FIG>, and the other material components are not displayed. Moreover, configuration may be made such that when plural item buttons in the item button array <NUM> have been selected, the plural selected material components are displayed, and other material components are not displayed.

The display control section <NUM> is configured to perform control to change a display settings mode in which, for example, the item button array <NUM> illustrated in <FIG> is operated to select material components that are not of interest (components not of interest) from among the plural material component images contained in the all-component image, so that the material component images that are components selected as not of interest are not displayed. The display control section <NUM> is configured to perform control such that a number of images exceeding an upper limit value are not displayed for material component images of images that are not of interest. As another example, material component images corresponding to item buttons that were not selected from the item button array <NUM> illustrated in <FIG> may be determined to be not of interest. Note that the upper limit value is set in advance for each of the material components. For example, in a case in which an upper limit value of <NUM> has been set for material component images representing white blood cells, then in a case in which <NUM> material component images representing white blood cells would be displayed in the all-component image if white blood cells have not been selected as not being of interest, the display control section <NUM> may perform control such that up to <NUM> of the material component images representing white blood cells are displayed but the <NUM>st image onward thereof are not displayed, or such that none of the material component images representing white blood cells are displayed, when white blood cells are not of interest. This enables a reduction in the number of material component images that are not of interest which are displayed, enabling greater emphasis to be placed on the material component images that are of interest.

Next, the setting section <NUM> sets an upper limit for the number of material component images for each category to be stored in the storage section <NUM> from out of the material component images categorized by the categorizing section <NUM>. Note that this upper limit number may be modified as appropriate. For example configuration may be made such that even if <NUM> instances of a particular material component have been counted, the number stored as images is <NUM>. If all the material component images for all the material components that had been counted were to be stored then the storage capacity of the storage section <NUM> might rapidly become insufficient therefor. It is thus desirable to store only the required number of the required material component images in each sample. Although such an approach could potentially lead to there being insufficient material component images to generate the all-component image, the ability to adjust the number of images stored by category in this manner would enable such issues to be addressed by measures such as increasing the number of images to be stored for material components that are of interest.

In cases in which the number of material component images for each category that are to be disposed as part of the all-component image exceeds the upper limit for the number of images stored in the storage section <NUM>, the generation section <NUM> is configured so as to duplicate material component images from out of the material component images stored in the storage section <NUM> so as to acquire material component images to make up the excess amount. Namely, in cases in which there are insufficient material component images, duplicates of the material component images stored in the storage section <NUM> are employed. Such cases include not only configurations in which simple duplication is performed, but also configurations in which processing is performed to rotate the orientation of such images, or enlarge or shrink such images.

Explanation follows regarding operation of the information processing device <NUM>, with reference to <FIG>.

<FIG> is a flowchart illustrating an example of a flow of processing by the image processing program 14A.

First, each of the following steps is executed when the information processing device <NUM> receives an instruction to start all-component image display processing.

At step <NUM> in <FIG>, the categorizing section <NUM> extracts material component images identified as each of the respective plural types of material components from the plural images acquired by imaging the sample fluid flowing through the flow cell <NUM> using the camera <NUM>. The material component images thus extracted are then categorized by category (for example, by material component type). The material component images that have been categorized by category by the categorizing section <NUM> are then stored in the storage section <NUM> as the material component image set 14B.

At step <NUM>, the count derivation section <NUM> derives for each category a count for the material components for a standard visual field or a count for the material components per unit liquid volume contained in the sample fluid, based on the numbers of material component images in each category as categorized at step <NUM>. Specifically, as an example, the number of material component images are converted into the counts per unit liquid volume by employing a calibration curve such as that illustrated in <FIG>. For example, the Equation (<NUM>) and Equation (<NUM>) described above are employed to derive the counts for a standard visual field from the converted counts per unit liquid volume.

At step <NUM>, the generation section <NUM> generates the all-component image by arranging the plural material component images so as not to overlap with each other, in numbers according to the counts per category derived at step <NUM>.

At step <NUM>, as an example the display control section <NUM> performs control to display the all-component image generated at step <NUM> as illustrated in <FIG>, and the sequence of processing by the image processing program 14Ais then ended.

Next, explanation follows regarding an example of a material component image list screen and a measurement result screen, with reference to <FIG> and <FIG>.

<FIG> is a face-on view illustrating an example of a material component image list screen <NUM>.

As illustrated in <FIG>, the material component image list screen <NUM> is a display of the material component images 14B by category. The material component image list screen <NUM> is displayed on the display section <NUM>.

Namely, as described above, the material component images categorized into each category by the categorizing section <NUM> are stored in the storage section <NUM> as the material component image set 14B. For example, when an operator wants to check the material component images for red blood cells, the operator selects a predetermined operation button (not illustrated in the drawings) to instruct the material component images 14B for red blood cells to be read from the storage section <NUM> and displayed as the material component image list screen <NUM>. Note that the material component image list screen <NUM> is similarly displayable for material components other than red blood cells, for example white blood cells and bacteria.

<FIG> is a face-on view illustrating an example of a measurement result screen <NUM>.

As illustrated in <FIG>, the measurement result screen <NUM> is a display of a measurement results table, together with an operation button 54A for displaying an all-component image. The measurement results shown include a contained amount or count for a standard visual field (HPF) as computed by the count derivation section <NUM>, a contained amount or count per unit liquid volume as computed thereby, and qualitative indices for contained amounts.

Main items among the items illustrated in <FIG> include, for example, RBC (red blood cells), WBC (white blood cells), NSE (non-squamous epithelial cells), SQEC (squamous epithelial cells), NHC (non-hyaline casts), and BACT (bacteria). Moreover, CRYS (crystals), YST (yeast), HYST (hyaline casts), MUCS (mucus), SPRM (spermatozoa), and WBCC (white blood cell clumps) are also illustrated.

The operation button 54A is a button labeled "VIEW ALL-COMPONENT IMAGE". When the operator selects the operation button 54A, as an example, the all-component image screen <NUM> illustrated in <FIG> described above is displayed.

<FIG> is a diagram to explain a process for generating the all-component image.

As illustrated at the top of <FIG>, plural of the material component images 14B are arranged according to the counts for each category so as to obtain an image <NUM>.

As illustrated at the bottom of <FIG>, the color tones of background colors and the like of each of the material component images 14B are adjusted in the image <NUM> so as to generate a single natural-looking all-component image <NUM>. The red blood cells, white blood cells, bacteria, casts, etc. serving as examples of material components are included in the all-component image <NUM> in numbers thereof similar to how they would appear if observed through a microscope.

Counts for material components imaged by the camera are derived per standard visual field for each category. This enables an all-component image with a feel similar to that of a microscope observation to be observed, even in cases in which image capture is performed using a flow method. Moreover, the ability to observe a screen displaying plural categorized elements imaged by the camera facilitates common properties and shapes (swollen, shriveled, etc.) to be ascertained in the plural material components, which would be more difficult to spot in individual extracted images.

Explanation has been given regarding an example of an information processing device according to an exemplary embodiment. The present exemplary embodiment may be provided in the format of a program configured to cause a computer to execute the functions with which the respective sections of the information processing device are equipped. The present exemplary embodiment may be provided in the format of a computer-readable storage medium stored with such a program.

Configurations of the information processing device described in the above exemplary embodiment are moreover merely examples thereof, and may be modified according to circumstances without departing from the scope of the present invention as defined in the appended claims.

The processing flow of the program described in the above exemplary embodiment is moreover also merely an example thereof, and steps not required may be omitted, new steps may be added, or the processing sequence may be altered without departing from the scope of the present invention as defined in the appended claims.

Claim 1:
An information processing device (<NUM>) for examining images taken from a biological sample, the information processing device (<NUM>) comprising:
a categorizing section (<NUM>) configured to:
receive a plurality of images obtained by imaging a sample fluid using a camera (<NUM>) whilst the sample fluid is flowing through a flow cell (<NUM>), the sample fluid containing a plurality of types of material components;
identify, from the plurality of images, a material component image set;
extract, from the plurality of images, the identified material component images; and
categorize the extracted material component images into predetermined categories, wherein the categorization is by at least one of: type, size, shape of material components, or presence of a nucleus;
a storage section (<NUM>) configured to store the extracted material component images categorized by the categorizing section (<NUM>);
a count derivation section (<NUM>) configured to compute a count of the material component in a visual field dependent on a magnification ratio, or compute a count per unit liquid volume of the material component contained in the flowing sample fluid, for each predetermined category based on the number of material component images categorized by the categorizing section (<NUM>);
a generation section (<NUM>) configured to generate an image in which stored material component images are displayed, the displayed material component images being arranged such that they do not overlap with each other, and in numbers according to the counts that have been computed by the count derivation section (<NUM>) for each predetermined category, in an area corresponding to the visual field; and
a setting section (<NUM>) configured to set an upper limit, for each of the categories, for a number of the material component images among the material component images that have been categorized by the categorizing section (<NUM>), to be stored in the storage section (<NUM>);
wherein the upper limit number of material component images stored is adjustable by category of material component; and
wherein the generation section (<NUM>) is configured to duplicate material component images stored in the storage section (<NUM>) in a case in which a number of material component images for each predetermined category to be displayed as part of the generated image exceeds the upper limit so as to acquire material component images for the amount by which the upper limit is exceeded.