Cell analysis system, method, and program

Abstract

To display measurement data so that the meaning of the measurement data can be easily understood.SOLUTION: According to an embodiment, a cell analysis system includes an acquisition unit configured to acquire a captured image obtained by imaging a cell, a detection unit configured to detect a measurement target image from the captured image, a measurement unit configured to measure a parameter of the measurement target based on the measurement target image, a generation unit configured to generate data based on registered measurement data registered in advance, and a display unit configured to display the measurement data measured by the measurement unit and the pseudo data corresponding to the measurement data on the same graph.SELECTED DRAWING: Figure 10

Description

[Technical Field]

[0001] The present invention relates to a cell analysis system, method, and program. [Background technology]

[0002] Conventionally, systems for analyzing cells from images have been used. Cited Document 1 discloses a data analysis device that includes a control unit that receives an operation to select analysis data acquired by an analysis device, a control unit that receives an operation to select multiple scripts to analyze the selected analysis data, and a control unit that executes analysis in parallel using the selected multiple scripts for the selected analysis data, and a display unit that displays on the same screen analysis results acquired by the analysis of the analysis data by the control unit. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2023-69333 Summary of the Invention [Problem to be solved by the invention]

[0004] Generally, measurement data such as cell size measured from an image of the object to be measured is displayed on a graph, which makes the measurement data visible and easy to understand.

[0005] However, there is a problem in that simply visualizing the measurement data makes it difficult for anyone other than those who have sufficient knowledge of the measurement data to understand the meaning of the measurement data.

[0006] The present invention has been made in view of the above-mentioned problems, and has an object to display measurement data in such a way that the meaning of the measurement data can be easily understood. [Means for solving the problem]

[0007] According to one embodiment, the cell analysis system includes an acquisition unit that acquires an image of a cell, a detection unit that detects an image of the measurement object from the image, a measurement unit that measures parameters of the measurement object based on the image of the measurement object, a generation unit that generates pseudo data based on pre-registered measurement data, and a display unit that displays the measurement data obtained by the measurement unit and the pseudo data corresponding to the measurement data on the same graph. [Effects of the Invention]

[0008] According to one embodiment, the measurement data can be displayed so that the meaning of the measurement data can be easily understood. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of a cell analysis system 1000. [Figure 2] 1 is a diagram illustrating an example of a hardware configuration of an information processing device 100. FIG. [Figure 3] FIG. 2 is a diagram illustrating an example of the functional configuration of the cell analyzer 1. [Figure 4] FIG. 10 is a diagram illustrating an example of measurement data. [Figure 5] FIG. 10 is a diagram showing an example of registered measurement data. [Figure 6] FIG. 10 is a diagram showing an example of registered measurement data. [Figure 7] FIG. 2 is a diagram illustrating an example of the functional configuration of a user terminal 2. [Figure 8] 1 is a flowchart showing an example of a cell analysis method. [Figure 9] FIG. 10 is a diagram illustrating an example of a graph. [Figure 10] FIG. 10 is a diagram showing another example of a graph. [Figure 11] FIG. 10 is a diagram showing an example of second pseudo data 127b. [Figure 12] FIG. 10 is a diagram showing another example of a graph. [Figure 13] FIG. 10 is a diagram showing another example of a graph. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, each embodiment of the present invention will be described with reference to the accompanying drawings. Note that, in the description of the specification and drawings relating to each embodiment, components having substantially the same functional configuration are designated by the same reference numerals, and redundant description will be omitted.

[0011] <System configuration> First, an overview of the cell analysis system 1000 according to this embodiment will be described. The cell analysis system 1000 is an information processing system that displays measurement data measured from an image of a measurement target included in a captured image and pseudo data on the same graph, thereby making it easy to understand the meaning of the measurement data.

[0012] The captured image is an image of a cell captured by an imaging device such as a microscope, and includes one or more images of the measurement object.

[0013] The measurement object image is an image of each measurement object included in the captured image. The measurement object may be, but is not limited to, a cell, a colony, a cell aggregate, a sheet, or a tissue.

[0014] The measurement data is data relating to parameters of the measurement object measured based on the measurement object image. The measurement data may be a measurement value for each parameter of the measurement object, or a statistical value for each parameter of multiple measurement objects included in the captured image.

[0015] The parameters of the measurement target include, but are not limited to, the number of measurement targets (cells, colonies, cell aggregates, sheets, tissues, etc.), size (area, perimeter, size of inscribed circle, size of circumscribed circle, etc.), shape (length of major axis ÷ length of minor axis, number of protrusions, length of perimeter ÷ size of inscribed circle, circularity, frequency, phase, amplitude obtained by Fourier transform, (perimeter^2) ÷ area, etc.), color, optical features (luminance distribution and pattern (GLCM: Gray-Level Co-occurrence Matrix, etc.), size of halo, size of bright spots, luminance gradation, etc.), structural features (ratio of cytoplasm to nucleus size, number of nuclei in a cell, size and number of intracellular vesicles relative to cytoplasm, number and length of protrusions per cell, etc.), and arrangement features (seeding density, seeding unevenness, mutual distance, orientation, etc.), or changes in these over time.

[0016] Pseudo data is simulated measurement data that is displayed on the same graph as the measurement data so that the meaning of the measurement data can be easily understood. The pseudo data is generated based on at least one of the measurement data and pre-registered measurement data. Details of the pseudo data will be described later.

[0017] Fig. 1 is a diagram showing an example of the configuration of a cell analysis system 1000. As shown in Fig. 1, the cell analysis system 1000 includes a cell analysis device 1 and a user terminal 2, which are communicably connected to each other via a network N. The network N is, for example, a wired local area network (LAN), a wireless LAN, the Internet, a public line network, a mobile data communication network, or a combination of these. In the example of Fig. 1, the cell analysis system 1000 includes one cell analysis device 1 and one user terminal 2, but may include multiple of each.

[0018] The cell analyzer 1 is an information processing device that acquires measurement data from captured images and generates pseudo data. The cell analyzer 1 is, for example, but not limited to, a PC (Personal Computer), a smartphone, a tablet terminal, a server device, or a microcomputer. In the example of FIG. 1, the cell analyzer 1 is a single information processing device, but may also be realized as a system consisting of multiple information processing devices connected via a network N.

[0019] The user terminal 2 is an information processing device used by a user of the cell analysis system 1000, and displays the measurement data and the simulated data on the same graph. The user may be, for example, a cell analyst, but is not limited to this. The user terminal 2 may be, for example, a PC, a smartphone, or a tablet terminal, but is not limited to these.

[0020] <Hardware configuration of information processing device 100> Next, a description will be given of the hardware configuration of the information processing device 100. Fig. 2 is a diagram showing an example of the hardware configuration of the information processing device 100. As shown in Fig. 2, the information processing device 100 includes a processor 101, a memory 102, a storage 103, a communication I / F 104, an input device 105, an output device 106, and a drive device 107, which are connected to each other via a bus B.

[0021] The processor 101 controls each component of the information processing device 100 and realizes the functions of the information processing device 100 by loading various programs including an OS (Operating System) stored in the storage 103 into the memory 102 and executing the programs. The processor 101 is, for example, but not limited to, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), or a DSP (Digital Signal Processor).

[0022] The memory 102 is, for example, a read-only memory (ROM), a random access memory (RAM), or a combination thereof. The ROM is, for example, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a combination thereof. The RAM is, for example, but not limited to, a dynamic random access memory (DRAM) or a static random access memory (SRAM).

[0023] The storage 103 stores various programs including an OS and data. The storage 103 is, for example, a flash memory, a hard disk drive (HDD), a solid state drive (SSD), or storage class memories (SCM), but is not limited to these.

[0024] The communication I / F 104 is an interface for connecting the information processing device 100 to an external device via the network N and controlling communication. The communication I / F 104 is, for example, Bluetooth (registered trademark), Wi-Fi (registered trademark), ZigBee (registered trademark), or Ethernet (registered trademark), but is not limited to these.

[0025] The input device 105 is a device for inputting information to the information processing device 100. The input device 105 is, for example, a mouse, a keyboard, a touch panel, a microphone, a scanner, an imaging device (camera), various sensors, or an operation button, but is not limited to these.

[0026] The output device 106 is a device for outputting information from the information processing device 100. The output device 106 is, for example, a display device, a projector, a printer, a speaker, or a vibrator, but is not limited to these.

[0027] The drive device 107 is a device that reads and writes data from and to the recording medium 108. The drive device 107 is, for example, but not limited to, a magnetic disk drive, an optical disk drive, a magneto-optical disk drive, or an SD card reader. The recording medium 108 is, for example, but not limited to, a CD (Compact Disc), a DVD (Digital Versatile Disc), an FD (Floppy Disk), an MO (Magneto-Optical disk), a BD (Blu-ray (registered trademark) Disc), a USB (registered trademark) memory, or an SD card.

[0028] In this embodiment, the program may be written to the memory 102 or the storage 103 during the manufacturing stage of the information processing device 100, or may be provided to the information processing device 100 via the network N, or may be provided to the information processing device 100 via a non-transitory computer-readable recording medium such as the recording medium 108.

[0029] <Functional configuration of cell analyzer 1> Next, a description will be given of the functional configuration of the cell analyzer 1. Fig. 3 is a diagram showing an example of the functional configuration of the cell analyzer 1. As shown in Fig. 3, the cell analyzer 1 includes a communication unit 11, a storage unit 12, and a control unit 13.

[0030] The communication unit 11 is realized by the communication I / F 104. The communication unit 11 transmits and receives information to and from the user terminal 2 via the network N.

[0031] The memory unit 12 is realized by a memory 102 and a storage 103. The memory unit 12 stores a captured image 121, captured image information 122, a measurement target image 123, measurement data 124, registered measurement data 125, registered captured image information 126, and pseudo data 127.

[0032] The photographed image 121 is a photographed image acquired from the user terminal 2. The storage unit 12 stores one or more photographed images 121.

[0033] The captured image information 122 is information about each captured image 121 stored in the storage unit 12. The captured image information 122 includes, for example, at least one of image information, capture information, cell information, and measurement target image information. The image information is information about the captured image itself, and includes, for example, information indicating the name, ID, size, resolution, type (extension), and creation date of the captured image. The capture information is information about cell capture, and includes, for example, information indicating the model of the capture device, the capture date and time, and the capture conditions (exposure amount, etc.). The cell information is information about the captured cell and its culture conditions, and the cell information includes, for example, information indicating the cell type, passage number, activity, and quality, and the culture condition information includes, for example, information indicating the medium, culture vessel, culture protocol, etc. The measurement target image information is information about the measurement target image detected from the captured image, and includes, for example, information indicating the ID, position, and size of the measurement target image. One piece of captured image information 122 includes measurement target image information for one or more measurement target images.

[0034] The measurement object image 123 is an image of the measurement object detected from the photographed image 121 .

[0035] The measurement data 124 is data relating to parameters of the measurement object measured based on the measurement object image 123 detected from the captured image 121. The measurement data 124 may be a measurement value for each parameter of each measurement object, or a statistical value for each parameter of multiple measurement objects included in the captured image 121. The number of parameters may be one or multiple. The parameter may be a principal component obtained by principal component analysis. The parameter may also be a profile of another parameter over time.

[0036] Fig. 4 is a diagram showing an example of measurement data 124. In Fig. 4, the measurement object is a cell, "PID" is the ID of the photographed image 124, "CID" is the ID of the measurement object image 123, and "size" and "color" are parameters. Photographed image P01 includes measurement object images C01 to C03, photographed image P02 includes measurement object images C04 to C06, and photographed image P03 includes measurement object images C07 to C09. The measurement data 124 in Fig. 4 includes measurement values ​​for each parameter of the measurement object corresponding to measurement object images C01 to C09, and statistical values ​​(average values) for each parameter of the three measurement objects included in photographed images P01 to P03.

[0037] Note that the measurement data 124 is not limited to the example in Fig. 4. The parameters may include parameters other than size and color, and the statistical values ​​may be other than the average value (such as mode, variance, standard deviation, CV, confidence interval, quantile, etc.).

[0038] The registered measurement data 125 is measurement data that is registered in advance to generate the first pseudo data 127a. The registered measurement data 125 preferably includes measurement data that includes values ​​(hereinafter referred to as "index values") that serve as indices of the measured values ​​or statistical values ​​of each parameter. Examples of index values ​​include, but are not limited to, maximum values, minimum values, average values, and modes. The first pseudo data 127a will be described later.

[0039] The registered measurement data 125 may be arbitrarily registered by an administrator of the cell analysis system 1000. Furthermore, past measurement data 124 may be registered as the registered measurement data 125. Furthermore, index values ​​for each condition, such as "maximum size (measured value) of cell X at passage number 1" and "minimum color (average value) of cell Y at passage number 2," may be registered as the registered measurement data 125.

[0040] Fig. 5 is a diagram showing an example of registered measurement data 125. In the example of Fig. 5, measurement data including the maximum measurement value of each parameter is registered as registered measurement data 125. In the example of Fig. 5, the maximum size value is 20, and the maximum color value is 200.

[0041] Fig. 6 is a diagram showing another example of registered measurement data 125. In the example of Fig. 6, past measurement data 124 (measurement data measured based on photographed images P91 to P93) is registered as registered measurement data 125.

[0042] The storage unit 12 may store the captured image and the measurement target image corresponding to the registered measurement data 125 in association with the registered measurement data 125. The captured image and the measurement target image corresponding to the registered measurement data 125 are the captured image and the measurement target image from which the registered measurement data 125 was measured.

[0043] The registered captured image information 126 is information about each captured image corresponding to the registered measurement data 125. The registered captured image information 126 includes, for example, at least one of image information, capture information, cell information, and measurement target image information. The image information is information about the captured image itself, including, for example, information indicating the name, ID, size, resolution, type (extension), and creation date of the captured image. The capture information is information about cell capture, including, for example, information indicating the model of the capture device, the capture date and time, and the capture conditions (exposure amount, etc.). The cell information is information about the captured cell and its culture conditions, including, for example, information indicating the cell type, passage number, activity, and quality, and information about the culture conditions including, for example, information indicating the medium, culture vessel, culture protocol, etc. The measurement target image information is information about the measurement target image detected from the captured image, including, for example, information indicating the ID, position, and size of the measurement target image. One registered captured image information 126 includes measurement target image information for one or more measurement target images.

[0044] The pseudo data 127 is pseudo-measurement data that is displayed on the same graph as the measurement data 124 so that the meaning of the measurement data 124 can be easily understood. The pseudo data 127 is generated based on at least one of the measurement data 124 and the registered measurement data 125. The pseudo data 127 includes first pseudo data 127a and second pseudo data 127b.

[0045] The first pseudo data 127a is pseudo measurement data including index values ​​of the measurement values ​​or statistical values ​​of each parameter, which is generated based on the registered measurement data 125. The first pseudo data 127a will be described in detail later.

[0046] The second pseudo data 127b is pseudo measurement data that is generated based on the measurement data 124 and indicates the distribution of the measurement data 124. The second pseudo data 127b may be generated based on the measurement data 124 and the registered measurement data 125. The second pseudo data 127b will be described in detail later.

[0047] The control unit 13 is realized by the processor 101 reading and executing a program from the memory 102 and working in cooperation with other hardware components. The control unit 13 controls the overall operation of the cell analyzer 1. The control unit 13 includes an acquisition unit 131, a detection unit 132, a measurement unit 133, and a generation unit 134.

[0048] The acquisition unit 131 acquires the captured image 221 from the user terminal 2 and stores it in the storage unit 12.

[0049] The detection unit 132 detects the measurement object image 123 from the captured image 121 and stores it in the storage unit 12 .

[0050] The measurement unit 133 measures parameters of the measurement object based on the measurement object image 123 and stores measurement data 124 obtained by the measurement in the storage unit 12 .

[0051] The generator 134 generates first pseudo data 127a based on the registered measurement data 125 and stores it in the storage unit 12. The generator 134 also generates second pseudo data 127b based on the measurement data 124 and stores it in the storage unit 12. The generator 134 may also generate second pseudo data 127b based on the measurement data 124 and the registered measurement data 125 and store it in the storage unit 12.

[0052] The functional configuration of the cell analyzer 1 is not limited to the above example. For example, the cell analyzer 1 may have some of the above functional configurations, with the user terminal 2 having the rest. The cell analyzer 1 may also have functional configurations other than those described above. Each functional configuration of the cell analyzer 1 may be realized by software, as described above, or by hardware such as an IC chip, a SoC (System on Chip), an LSI (Large Scale Integration), or a microcomputer.

[0053] <Functional configuration of user terminal 2> Next, a description will be given of the functional configuration of the user terminal 2. Fig. 7 is a diagram showing an example of the functional configuration of the user terminal 2. As shown in Fig. 7, the user terminal 2 includes a communication unit 21, a storage unit 22, and a control unit 23.

[0054] The communication unit 21 is realized by the communication I / F 104. The communication unit 21 transmits and receives information to and from the cell analyzer 1 via the network N.

[0055] The storage unit 22 is realized by the memory 102 and the storage 103. The storage unit 22 stores a captured image 221, captured image information 222, measurement data 223, and simulation data 224.

[0056] The photographed image 221 is a photographed image prepared by the user for analysis. The storage unit 22 stores one or more photographed images 221.

[0057] The photographed image information 222 is information about each photographed image 221 stored in the storage unit 22. The photographed image information 222 includes, for example, at least one of image information, photographing information, and cell information. The image information is information about the photographed image itself, and includes, for example, information indicating the name, ID, size, resolution, type (extension), and creation date of the photographed image. The photographing information is information about photographing the cells, and includes, for example, information indicating the model of the photographing device, the date and time of photographing, and the photographing conditions (exposure amount, etc.). The cell information is information about the photographed cells, and includes, for example, information indicating the type of cell, the passage number, and the culture medium. The photographed image information 222 may be automatically acquired together with the measurement target image 221 from the photographing device that photographed the cells, or may be manually registered by the user.

[0058] The measurement data 223 is measurement data acquired from the cell analyzer 1.

[0059] The pseudo data 224 is pseudo data acquired from the cell analyzer 1. The pseudo data 224 corresponds to the measurement data 223.

[0060] The control unit 23 is realized by the processor 101 reading and executing a program from the memory 102 and working in cooperation with other hardware components. The control unit 23 controls the overall operation of the user terminal 2. The control unit 23 includes a display unit 231.

[0061] The display unit 231 displays a screen in response to a user operation on the display device of the user terminal 2. In this embodiment, the display unit 231 displays the measurement data 223 and the pseudo data 224 corresponding to the measurement data 223 on the same graph.

[0062] The functional configuration of the user terminal 2 is not limited to the above example. For example, the user terminal 2 may have some of the above functional configurations, with the rest being provided by the cell analyzer 1. The user terminal 2 may also have functional configurations other than those described above. Each functional configuration of the user terminal 2 may be realized by software, as described above, or by hardware such as an IC chip, SoC, LSI, or microcomputer.

[0063] <Cell analysis method> Next, a cell analysis method will be described. Fig. 8 is a flowchart showing an example of processing executed by the cell analysis system 1000.

[0064] (Step S101) The user operates the user terminal 2 to upload the photographed image 221 and the photographed image information 222 corresponding to the photographed image 221, which are stored in the storage unit 22, to the cell analyzer 1. The user terminal 2 transmits (uploads) the photographed image 221 and the photographed image information 222 to the cell analyzer 1.

[0065] (Step S102) When the acquisition unit 131 of the cell analyzer 1 receives (acquires) the photographed image 221 and the photographed image information 222 from the user terminal 2, the acquisition unit 131 stores them in the storage unit 12 as the photographed image 121 and the photographed image information 122.

[0066] (Step S103) The detection unit 132 detects one or more measurement target images 123 from the captured images 121 stored in the storage unit 12, and stores the detected images in the storage unit 12. The detection unit 132 detects the measurement target images 123 by using, for example, a machine learning model that has learned the measurement target images. The detection unit 132 can use any method as a method for detecting the measurement target images 123.

[0067] Furthermore, the detection unit 132 adds measurement object image information according to the detection result to the captured image information 122. As a result, the captured image information 122 is updated.

[0068] (Step S104) The measurement unit 133 measures parameters of the measurement object based on the measurement object image 123 stored in the storage unit 12, and stores measurement data 124 obtained by the measurement in the storage unit 12. The measurement unit 133 can use any method as a method for measuring the parameters.

[0069] (Step S105) The user operates the user terminal 2 to select graph conditions. The graph conditions are conditions for the graph to be displayed on the display device of the user terminal 2. The graph conditions are, for example, but are not limited to, the type of graph (bar graph, pie chart, scatter plot, etc.), the presence or absence of pseudo data 127, the type of value (measured value or statistical value), identification information of the measurement data 223 to be displayed (ID of the captured image 121 or the measurement target image 123), or conditions for the measurement data 223 to be displayed (cell type, passage number, culture medium, etc.). The identification information and conditions for the measurement data 223 are selected from the captured image information 122.

[0070] (Step S106) The user operates the user terminal 2 to request the cell analyzer 1 to display a graph. The user terminal 2 transmits the graph display request to the cell analyzer 1 together with the graph conditions.

[0071] (Step S107) When a request is made to display a graph including the pseudo data 127 , the generating unit 134 generates the pseudo data 127 according to the graph conditions and stores it in the storage unit 12 .

[0072] (Step S108) The generation unit 134 transmits the measurement data 124 selected by the graph conditions and the pseudo data 127 corresponding to this measurement data 124 to the user terminal 2. At this time, the generation unit 134 may also transmit the measurement target image 123 corresponding to the measurement data 124 and the registered captured image information 126 corresponding to the pseudo data 127 to the user terminal 2.

[0073] (Step S109) When the display unit 231 of the user terminal 2 receives the measurement data 124 and the simulated data 127 from the cell analyzer 1, it stores them in the memory unit 22 as measurement data 223 and simulated data 224, and displays a graph according to the graph conditions on the display device.

[0074] Here, Fig. 9 is a diagram showing an example of a graph displayed on the user terminal 2. Fig. 9 is a bar graph showing the measurement values ​​(measurement data 223) of the colors (parameters) of the cells (measurement target image 123) included in the captured image P01. Fig. 9(a) is a graph when there is no pseudo data 224, and Fig. 9(b) is a graph when there is pseudo data 224.

[0075] 9(a), when there is no pseudo data 224, only the color measurement values ​​of the measurement target images C01 to C03 are displayed on the graph. If the user does not have sufficient knowledge about color measurement values, the user will not understand the meaning of the measurement data 223 even when looking at the graph in FIG.

[0076] 9(b), the first pseudo data 224a is displayed together with the color measurement values ​​of the measurement target images C01 to C03. The dotted line L1 indicates the maximum color value (first pseudo data 224a), and the dotted line L2 indicates the minimum color value (first pseudo data 224a).

[0077] 5, when index values ​​are stored as registered measurement data 125, generation unit 134 refers to registered captured image information 126 and selects, as first pseudo data 127a, registered measurement data 125 that includes index values ​​corresponding to measurement data 124. In the example of FIG. 9(b), registered measurement data 125 that includes the maximum and minimum values ​​of the measurement values ​​of the color of cells of the same type as measurement target images C01 to C03 is selected as first pseudo data 127a and displayed on the graph as first pseudo data 224a.

[0078] 6, when past measurement data is stored as registered measurement data 125, generation unit 134 refers to registered photographed image information 126 to select registered measurement data 125 corresponding to measurement data 124, and selects registered measurement data 125 that will serve as an index value from the selected registered measurement data 125 as first pseudo data 127a. In the example of FIG. 9(b), from registered measurement data 125 that include measurement values ​​of the colors of cells of the same type as measurement target images C01 to C03, registered measurement data 125 that include the maximum and minimum color values ​​is selected as first pseudo data 127a and displayed on the graph as first pseudo data 224a.

[0079] In this way, by displaying the measurement data 223 and the first pseudo data 224a, which is an index value, on the same graph, even if the user does not have sufficient knowledge about color measurement values, the user can easily understand the meaning of the measurement data 223. For example, in the example of Fig. 9(b), it is easy to see that the color values ​​of the measurement target images C01 to C03 tend to be low overall.

[0080] Fig. 10 is a diagram showing another example of a graph displayed on the user terminal 2. Fig. 10 is a scatter plot showing statistical values ​​(measurement data 223) of the colors and sizes (parameters) of cells (measurement target images 123) included in the captured images P01 to P03. Fig. 10(a) is a graph when there is no pseudo data 224, and Fig. 10(b) is a graph when there is pseudo data 224.

[0081] 10(a), when there is no pseudo data 224, only the color and size statistics of the captured images P01 to P03 are displayed on the graph. If the user does not have sufficient knowledge about the color and size statistics, the user will not understand the meaning of the measurement data 223 even when looking at the graph in FIG. 10(a).

[0082] 10(b), the first pseudo data 224a is displayed along with the color and size statistics of the captured images P01 to P03, which are represented by circles. The dotted line L1 indicates the maximum color value (first pseudo data 224a), the dotted line L2 indicates the minimum color value (first pseudo data 224a), the dotted line L3 indicates the maximum size (first pseudo data 224a), and the dotted line L4 indicates the minimum size (first pseudo data 224a).

[0083] 5, when index values ​​are stored as registered measurement data 125, generation unit 134 refers to registered photographed image information 126 and selects, as first pseudo data 127a, registered measurement data 125 that includes index values ​​corresponding to measurement data 124. In the example of FIG. 9(b), registered measurement data 125 that includes maximum and minimum values ​​of the measurement values ​​of the color and size of cells of the same type as photographed images P01 to P03 is selected as first pseudo data 127a and displayed on the graph as first pseudo data 224a.

[0084] 6, when past measurement data is stored as registered measurement data 125, generation unit 134 refers to registered photographed image information 126 to select registered measurement data 125 corresponding to measurement data 124, and selects registered measurement data 125 that serves as an index value from the selected registered measurement data 125 as first pseudo data 127a. In the example of FIG. 10(b), from registered measurement data 125 that include measurement values ​​of the color and size of cells of the same type as photographed images P01 to P03, registered measurement data 125 that includes maximum and minimum values ​​of color and size is selected as first pseudo data 127a and displayed on a graph as first pseudo data 224a.

[0085] 6, when past measurement data is stored as registered measurement data 125, the generation unit 134 may refer to the registered photographed image information 126 to select registered measurement data 125 corresponding to the measurement data 124, select multiple registered measurement data 125 from the selected registered measurement data 125 according to the index value, and calculate first pseudo data 127a based on the selected multiple registered measurement data 125. In the example of FIG. 10(b), multiple registered measurement data 125 with large color values ​​(measurement target images C97-C99) are selected from registered measurement data 125 containing color and size measurement values ​​of the same type of cells as photographed images P01-P03, and the average color measurement value (177.6) of the selected registered measurement data 125 is calculated as the maximum color statistical value (first pseudo data 127a) and displayed on the graph as first pseudo data 224a. The same applies to the minimum color value, maximum size value, and minimum size value.

[0086] In this way, by displaying the measurement data 223 and the first pseudo data 224a, which is an index value, on the same graph, even if the user does not have sufficient knowledge about the measurement values ​​of color and size, the user can easily understand the meaning of the measurement data 223. For example, in the example of Fig. 10(b), it can be easily understood that the color and size values ​​of the cells included in the photographed image P01 are very small among cells of this type, and that the color and size values ​​of the cells included in the photographed images P02 and P03 are average among cells of this type.

[0087] 10(b), the second pseudo data 224b is displayed together with the statistical values ​​of the colors and sizes of the captured images P01 to P03, which are represented by circles. Each of the multiple squares sq represents the second pseudo data 224b.

[0088] The generator 134 generates second pseudo data 127b from the measurement data 124 using a method such as bootstrap. In the example of Fig. 10(b), the second pseudo data 127b is generated based on the measurement data 124 of the captured images P01 to P03, and is displayed on a graph as second pseudo data 224b. Note that the method of generating the second pseudo data 127b is not limited to bootstrap.

[0089] Fig. 11 is a diagram showing a schematic diagram of a method for generating second pseudo data 127b. In the example of Fig. 11, second pseudo data 127b is generated by bootstrapping. Specifically, three measurement data 124 are randomly selected from the measurement data 124 of captured images P01 to P03, and the average value of the three measurement data 124 is calculated. For example, the first second pseudo data 127b is second pseudo data 127b that includes the average values ​​of the colors and sizes of measurement target images C01, C05, and C06.

[0090] By generating second pseudo data 127b in this manner, pseudo measurement data indicating the distribution of measurement data 124 can be generated from measurement data 124 alone.

[0091] 6, when past measurement data is stored as registered measurement data 125, generation unit 134 may refer to registered photographed image information 126 to select registered measurement data 125 corresponding to measurement data 124, and generate second pseudo data 127b by a method such as bootstrap based on the selected registered measurement data 125 and measurement data 124. In the example of FIG. 10(b), second pseudo data 127b is generated based on measurement data 124 of photographed images P01 to P03 and registered measurement data 125 including measurement values ​​of the color and size of cells of the same type as those of photographed images P01 to P03, and is displayed on a graph as second pseudo data 224b.

[0092] By generating the second pseudo data 127b in this manner, pseudo measurement data that indicates the distribution of the measurement data 124 and is generalized based on past measurement data can be generated.

[0093] 6, when past measurement data is stored as registered measurement data 125, generation unit 134 may refer to registered photographed image information 126 to select registered measurement data 125 corresponding to measurement data 124, select registered measurement data 125 similar to measurement data 124 from the selected registered measurement data 125, and generate second pseudo data 127b by a method such as bootstrap based on the selected registered measurement data 125 and measurement data 124. In the example of FIG. 10(b), second pseudo data 127b is generated based on measurement data 124 of photographed images P01 to P03 and registered measurement data 125 containing measurement values ​​of the color and size of cells of the same type as photographed images P01 to P03 that is similar to measurement data 124, and is displayed on a graph as second pseudo data 224b.

[0094] By generating second pseudo data 127b in this manner, pseudo measurement data showing the distribution of measurement data 124 in which the characteristics of measurement data 124 are emphasized can be generated.

[0095] As described above, by displaying the measurement data 223 and the second pseudo data 224b, which is pseudo measurement data showing the distribution of the measurement data 223, on the same graph, even if the user does not have sufficient knowledge about the measurement values ​​of color and size, the user can easily understand the meaning of the measurement data 223. For example, in the example of Fig. 10(b), it can be easily seen that the color and size values ​​of the cells included in the photographed images P01 to P03 are distributed in an arc shape, and are located near the start point, apex, and end point of the arc, respectively.

[0096] Fig. 12 is a diagram showing another example of a graph displayed on the user terminal 2. Fig. 12 is a scatter plot obtained by principal component analysis of the measured values ​​of three parameters (size, color, and GCLM) of each measurement target corresponding to measurement target images C01 to C09. Fig. 12(a) is a graph when there is no pseudo data 224, and Fig. 12(b) is a graph when there is pseudo data 224. P1 is the first principal component, and P2 is the second principal component.

[0097] 12(a), only the measurement data 223 of the measurement target images C01 to C09 are displayed on the graph, and the nine measurement data 223 are classified into two clusters CL1 and CL2. On the other hand, in FIG. 12(b), the measurement data 223 of the measurement target images C01 to C09 and the first pseudo data 224a (measurement target images C41 and C76) are displayed on the graph, and the eleven measurement data 223 are classified into four clusters CL11 to CL14.

[0098] In this way, when performing principal component analysis on parameters, by displaying the measurement data 223 and the first pseudo data 224a on the same graph, it is possible to show correlations that cannot be seen from the measurement data 223 alone.

[0099] Fig. 13 is a diagram showing another example of a graph displayed on the user terminal 2. Fig. 13 is a heat map obtained by hierarchically clustering the measurement values ​​of three parameters (size, color, and GCLM) of each measurement object corresponding to measurement object images C01 to C09. Fig. 13(a) is a graph when there is no pseudo data 224, and Fig. 13(b) is a graph when there is pseudo data 224.

[0100] In FIG. 13(a), only the measurement data 223 of measurement target images C01 to C09 are displayed on the graph, and the nine measurement data 223 are classified into two clusters CL1 and CL2. In FIG. 13(a), the pattern of the measurement data 223 is unclear. On the other hand, in FIG. 13(b), the measurement data 223 of measurement target images C01 to C09 and the first pseudo data 224a (measurement target images C41 and C76) are displayed on the graph, and the eleven measurement data 223 are classified into four clusters CL11 to CL14. It can be seen that the pattern of the measurement data 223 is clearer in FIG. 13(b) than in FIG. 13(a).

[0101] In this way, when the measurement data 223 is displayed as a heat map, by displaying the measurement data 223 and the first pseudo data 224a on the same graph, it is possible to show patterns in the measurement data 223 that cannot be seen from the measurement data 223 alone.

[0102] <Summary> As described above, according to this embodiment, a cell analysis system 1000 can be realized which includes an acquisition unit 131 which acquires a captured image 121 of a cell, a detection unit 132 which detects a measurement object image 123 from the captured image 121, a measurement unit 133 which measures parameters of the measurement object based on the measurement object image 123, a generation unit 134 which generates pseudo data 127 based on pre-registered measurement data 125, and a display unit 231 which displays the measurement data 124 from the measurement unit 133 and the pseudo data 127 corresponding to the measurement data 124 on the same graph.

[0103] By displaying the measurement data 124 and the pseudo data 127 on the same graph by the cell analysis system 1000, even a user who does not have sufficient knowledge about the measurement data 124 can easily understand the meaning of the measurement data 124.

[0104] <Additional Notes> The present embodiment includes the following disclosure.

[0105] (Appendix 1) an acquisition unit that acquires a captured image of a cell; a detection unit that detects a measurement target image from the captured image; a measurement unit that measures parameters of the measurement object based on the measurement object image; a generating unit that generates pseudo data based on pre-registered measurement data; a display unit that displays the measurement data obtained by the measurement unit and the pseudo data corresponding to the measurement data on the same graph; A cell analysis system comprising:

[0106] (Appendix 2) The display unit displays the measurement data and the pseudo data in different formats. 2. The cell analysis system of claim 1.

[0107] (Appendix 3) The generating unit generates first pseudo data including index values ​​of the parameters based on the registered measurement data. 2. The cell analysis system of claim 1.

[0108] (Appendix 4) The generating unit generates second pseudo data indicating a distribution of the measurement data based on the measurement data. 2. The cell analysis system of claim 1.

[0109] (Appendix 5) The generating unit generates second pseudo data indicating a distribution of the measurement data based on the measurement data and the registered measurement data. 2. The cell analysis system of claim 1.

[0110] (Appendix 6) The parameters include at least one of the size, shape, and color of the object. 2. The cell analysis system of claim 1.

[0111] (Appendix 7) A cell analysis method performed by a cell analysis system, comprising: A process of acquiring a photographed image of the cell; A process of detecting an image of a measurement target from the captured image; a process of measuring a parameter of the measurement object based on the measurement object image; A process of generating pseudo data based on previously registered measurement data; a process of displaying the measurement data obtained by the measuring process and the pseudo data corresponding to the measurement data on the same graph; A cell analysis method comprising:

[0112] (Appendix 8) Cell analysis system, A process of acquiring a photographed image of the cell; A process of detecting an image of a measurement target from the captured image; a process of measuring a parameter of the measurement object based on the measurement object image; A process of generating pseudo data based on previously registered measurement data; a process of displaying the measurement data obtained by the measuring process and the pseudo data corresponding to the measurement data on the same graph; A program for executing a cell analysis method comprising:

[0113] The embodiments disclosed herein are illustrative in all respects and should not be considered limiting. The scope of the present invention is defined by the claims, not by the above meaning, and is intended to include all modifications within the meaning and scope of the claims. Furthermore, the present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. [Explanation of symbols]

[0114] 1:Cell analyzer 2: User terminal 121: Photographed image 122: Image information 123: Measurement target image 124: Measurement data 125: Registered measurement data 126: Registered photographic image information 127: Pseudo data 131: Acquisition Department 132: Detection unit 133: Measuring part 134: Generation part

Claims

1. an acquisition unit that acquires a captured image of a cell; a detection unit that detects a measurement target image from the captured image; a measurement unit that measures parameters of the measurement object based on the measurement object image; a generating unit that generates pseudo data based on pre-registered measurement data; a display unit that displays the measurement data obtained by the measurement unit and the pseudo data corresponding to the measurement data on the same graph; A cell analysis system comprising:

2. The display unit displays the measurement data and the pseudo data in different formats. The cell analysis system of claim 1 .

3. The generating unit generates first pseudo data including index values ​​of the parameters based on the registered measurement data. The cell analysis system of claim 1 .

4. The generating unit generates second pseudo data indicating a distribution of the measurement data based on the measurement data. The cell analysis system of claim 1 .

5. The generating unit generates second pseudo data indicating a distribution of the measurement data based on the measurement data and the registered measurement data. The cell analysis system of claim 1 .

6. The parameters include at least one of the size, shape, and color of the measurement object. The cell analysis system of claim 1 .

7. A cell analysis method performed by a cell analysis system, comprising: A process of acquiring a photographed image of the cell; A process of detecting an image of a measurement target from the captured image; a process of measuring a parameter of the measurement object based on the measurement object image; A process of generating pseudo data based on previously registered measurement data; a process of displaying the measurement data obtained by the measuring process and the pseudo data corresponding to the measurement data on the same graph; A cell analysis method comprising:

8. Cell analysis system, A process of acquiring a photographed image of the cell; A process of detecting an image of a measurement target from the captured image; a process of measuring a parameter of the measurement object based on the measurement object image; A process of generating pseudo data based on previously registered measurement data; a process of displaying the measurement data obtained by the measuring process and the pseudo data corresponding to the measurement data on the same graph; A program for executing a cell analysis method comprising:

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