Information processing program, information processing device, information processing method, and microscope system

Abstract

[Problem] To provide an information processing program, an information processing device, an information processing method, and a microscope system, which are capable of more effective parameter adjustment. [Solution] An information processing program causes a computer to execute an acquisition step for acquiring a first image by image-capturing of a specimen tissue, an analysis processing step for performing analysis processing the first image using a predetermined parameter, and a determination step for determining whether to perform the analysis processing by the predetermined parameter, on the basis of first property information of tissue morphology present in the first image, and second property information of a tissue morphology corresponding to the predetermined parameter.

Description

[Technical Field]

[0001] The present disclosure relates to an information processing program, an information processing device, an information processing method, and a microscope system. [Background technology]

[0002] There is a known microscope system that uses a microscope to capture images of an object placed on a glass slide and generate a digitized pathological image called a Whole Slide Imaging (WSI) image. This microscope system can extract information by performing various types of image analysis. In order to perform various types of image analysis on this microscope system, the user must adjust the parameters. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-007849 Summary of the Invention [Problem to be solved by the invention]

[0004] However, adjusting parameters using all tissue regions on a WSI image takes a long time. Therefore, parameters can be adjusted more quickly by using a small region of interest (ROI) on the WSI image. However, this parameter adjustment may increase the processing load on the observer. Therefore, the present disclosure provides an information processing program, an information processing device, an information processing method, and a microscope system that enable more efficient parameter adjustment. [Means for solving the problem]

[0005] In order to solve the above problems, according to the present disclosure, there is provided a method for detecting a tissue sample by an imaging method, comprising: an analysis processing step of analyzing the first image using predetermined parameters; a determination step of determining whether to perform the analysis process using the predetermined parameters based on first characteristic information of tissue morphology present in the first image and second characteristic information of tissue morphology corresponding to the predetermined parameters; An information processing program for causing a computer to execute the above is provided.

[0006] The determining step may determine not to perform the analysis process using the predetermined parameters when the first characteristic information and the second characteristic information are not within a predetermined distance on a statistical distribution.

[0007] The method may further include a display control step of, when the determination step determines that the analysis process should not be performed using the specified parameters, causing a display unit to display a display format recommending setting new parameters for the analysis process.

[0008] the first characteristic information is generated based on an image within a predetermined region of the first image, The display form may be at least one of an image within the predetermined area and an image showing the predetermined area.

[0009] As the display format, a message recommending that the parameter be set may be displayed.

[0010] The display control step may include displaying an image indicating position information of the first characteristic information within the statistical distribution and the display format side by side on the display unit.

[0011] the first characteristic information is a plurality of pieces of characteristic information selected from a plurality of pieces of characteristic information based on a plurality of processing regions in the first image, The display control step may cause the display unit to display images in the processing area corresponding to the selected plurality of pieces of characteristic information in a row.

[0012] the first characteristic information is a plurality of pieces of characteristic information selected from each clustering area by clustering a plurality of pieces of characteristic information based on each of a plurality of processing areas in the first image into a plurality of areas, The display control step may include displaying the processing area for each of the clustered areas in association with each other on the first image.

[0013] the first characteristic information is a plurality of pieces of characteristic information selected from a plurality of pieces of characteristic information based on a plurality of processing regions in the first image, The determination step may change the determination criteria so that the first characteristic information is not selected when it is determined that the analysis process should not be performed using the specified parameters and parameters based on the processing area corresponding to the first characteristic information are not used in the analysis process.

[0014] the first characteristic information is a plurality of pieces of characteristic information selected from a plurality of pieces of characteristic information based on a plurality of processing regions in the first image, The determination step may also include changing the imaging conditions of the imaging device that captured the first image so that the first characteristic information is not selected when it is determined that the analysis process should not be performed using the specified parameters and parameters based on the processing area corresponding to the first characteristic information are not used in the analysis process.

[0015] The second characteristic information may be a plurality of pieces of characteristic information calculated from different captured images.

[0016] The determining step may change the predetermined distance depending on the number of the second characteristic information corresponding to the predetermined parameter.

[0017] a storage step of storing a plurality of parameters used in the analysis processing step in a storage unit in association with the corresponding second characteristic information; When the judgment step determines that the analysis process should not be performed using the specified parameters, new parameters for the analysis process corresponding to the first characteristic information may be stored in the memory unit in association with the first characteristic information.

[0018] When it is determined that the analysis process should not be performed using the specified parameters, the judgment process may determine whether characteristic information similar to the first characteristic information is stored in the memory unit, and if not, store new parameters for the analysis process corresponding to the first characteristic information in the memory unit.

[0019] The determination step may include, when it is determined that characteristic information similar to the first characteristic information is stored in the storage unit, performing the analysis process based on a parameter corresponding to the similar characteristic information.

[0020] The determining step may include performing the analysis process using the predetermined parameters when the first characteristic information and the second characteristic information are within a predetermined distance on a statistical distribution.

[0021] The method may further include a display control step of, when the determining step determines that the analysis processing is to be performed using the predetermined parameters, causing a display unit to display a display format indicating that the analysis processing is possible using the predetermined parameters.

[0022] The first characteristic information and the second characteristic information may be at least one of a brightness value, a cell density, a cell circularity, a cell perimeter, and a local feature, and the distance may be at least one of a Mahalanobis distance and a Euclidean distance.

[0023] In order to solve the above problems, according to the present disclosure, there is provided an imaging system including: an acquisition unit that acquires a first image of a sample tissue; an analysis processing unit that analyzes and processes the first image using predetermined parameters; a determination unit that determines whether to perform the analysis process using the predetermined parameters based on first characteristic information of tissue morphology present in the first image and second characteristic information of tissue morphology corresponding to the predetermined parameters; An information processing device comprising:

[0024] In order to solve the above problems, according to the present disclosure, there is provided a microscope apparatus for acquiring a first image of a specimen tissue, An information processing device, The information processing device includes: an acquisition unit that acquires a first image of the sample tissue; an analysis processing unit that analyzes and processes the first image using predetermined parameters; a determination unit that determines whether to perform the analysis process using the predetermined parameters based on first characteristic information of tissue morphology present in the first image and second characteristic information of tissue morphology corresponding to the predetermined parameters; A microscope system is provided, comprising: [Brief explanation of the drawings]

[0025] [Figure 1] FIG. 1 is a diagram schematically illustrating the overall configuration of a microscope system. [Figure 2] FIG. 1 is a diagram showing an example of an imaging method. [Figure 3] FIG. 1 is a diagram showing an example of an imaging method. [Figure 4] FIG. 4 is a block diagram showing a more detailed configuration example of an information processing unit. [Figure 5] FIG. 10 is a diagram showing an example of a plurality of ROIs for calculating feature amounts. [Figure 6] FIG. 10 is a diagram showing an example of a user interface for selecting characteristic information for selecting a feature amount. [Figure 7] FIG. 4 is a diagram schematically showing the distribution of feature amounts of each ROI calculated by a feature amount calculation unit. [Figure 8] FIG. 10 is a diagram schematically showing the position of a representative ROI within a feature distribution. [Figure 9] FIG. 10 is a diagram schematically showing positions in the feature distribution corresponding to different parameters in the representative ROI. [Figure 10] 4 is a flowchart showing an example of processing by the information processing device according to the first embodiment. [Figure 11] FIG. 10 is a block diagram showing an example of the arrangement of an information processing unit according to the second embodiment. [Figure 12] FIG. 10 is a diagram showing an example of a screen area. [Figure 13] FIG. 13 is a diagram showing an example in which specific information is displayed in each area shown in FIG. 12. [Figure 14] 10 is a flowchart showing an example of processing by an information processing device according to the second embodiment. [Figure 15] 10 is a flowchart showing an example of processing performed by an information processing device when a recommended ROI is not adopted. [Figure 16] FIG. 11 is a block diagram showing an example of the arrangement of an information processing device 5120 according to the third embodiment. [Figure 17] 10 is a flowchart showing an example of re-learning of the information processing device 5120 when the recommended ROI is not adopted. [Figure 18] 10 is a flowchart showing an example of determining whether similar information exists in the characteristic information stored in the storage unit. [Figure 19] 10A and 10B are diagrams showing examples of determination distances used in a similarity determination processing unit. DETAILED DESCRIPTION OF THE INVENTION

[0026] Hereinafter, embodiments of an information processing program, an information processing device, an information processing method, and a microscope system will be described with reference to the drawings. The following description will focus on the main components of the information processing program, the information processing device, the information processing method, and the microscope system, but the information processing program, the information processing device, the information processing method, and the microscope system may include components and functions that are not shown or described. The following description does not exclude components and functions that are not shown or described.

[0027] (First embodiment) FIG. 1 shows an example configuration of a microscope system according to the present disclosure. The microscope system 5000 shown in FIG. 1 includes a microscope device 5100, a control unit 5110, and an information processing unit 5120. The microscope device 5100 includes a light irradiation unit 5101, an optical unit 5102, and a signal acquisition unit 5103. The microscope device 5100 may further include a sample mounting unit 5104 on which a biological sample S is placed. The configuration of the microscope device is not limited to that shown in FIG. 1. For example, the light irradiation unit 5101 may be located outside the microscope device 5100, and a light source not included in the microscope device 5100 may be used as the light irradiation unit 5101. The light irradiation unit 5101 may be arranged so that the sample mounting unit 5104 is sandwiched between the light irradiation unit 5101 and the optical unit 5102. For example, the light irradiation unit 5101 may be arranged on the side where the optical unit 5102 is located. The microscope device 5100 may be configured with one or more of bright-field observation, phase-contrast observation, differential interference observation, polarized light observation, fluorescent observation, and dark-field observation.

[0028] The microscope system 5000 may be configured as a so-called WSI (Whole Slide Imaging) system or a digital pathology system and may be used for pathological diagnosis. The microscope system 5000 may also be configured as a fluorescence imaging system, particularly a multiplex fluorescence imaging system.

[0029] For example, the microscope system 5000 may be used to perform intraoperative pathological diagnosis or remote pathological diagnosis. In the intraoperative pathological diagnosis, the microscope device 5100 may acquire data of a biological sample S obtained from a subject of the surgery while the surgery is being performed, and transmit the data to an information processing unit 5120. In the remote pathological diagnosis, the microscope device 5100 may transmit data of the acquired biological sample S to an information processing device 5120 located in a location remote from the microscope device 5100 (such as a different room or building). In these diagnoses, the information processing device 5120 receives and outputs the data. A user of the information processing device 5120 may perform a pathological diagnosis based on the output data.

[0030] (biological samples) The biological sample S may be a sample containing a biological component, which may be a biological tissue, a cell, a liquid component (such as blood or urine), a culture, or a living cell (such as a cardiac muscle cell, a nerve cell, or a fertilized egg). The biological sample may be a solid, such as a specimen fixed with a fixative such as paraffin, or a solid formed by freezing. The biological sample may be a section of the solid. A specific example of the biological sample is a section of a biopsy sample.

[0031] The biological sample may be one that has been subjected to a process such as staining or labeling. The process may be staining to reveal the morphology of the biological component or to reveal substances (such as surface antigens) contained in the biological component, and examples of such staining include hematoxylin-eosin (HE) staining and immunohistochemistry staining. The biological sample may be one that has been subjected to the process using one or more reagents, and the reagents may be fluorescent dyes, color-developing reagents, fluorescent proteins, or fluorescently labeled antibodies.

[0032] The specimen may be prepared from a specimen or tissue sample collected from the human body for the purpose of pathological diagnosis or clinical testing. Furthermore, the specimen is not limited to being derived from the human body, but may also be derived from animals, plants, or other materials. The characteristics of the specimen vary depending on the type of tissue (e.g., organ or cell) used, the type of disease being treated, the subject's attributes (e.g., age, sex, blood type, or race), or the subject's lifestyle (e.g., diet, exercise, or smoking habits). The specimens may be managed with identifying information (e.g., barcode information or QR code (trademark) information) that allows each specimen to be identified.

[0033] (Light irradiation part) The light irradiation unit 5101 is a light source for illuminating the biological sample S and an optical unit for guiding the light irradiated from the light source to the specimen. The light source can irradiate the biological sample with visible light, ultraviolet light, infrared light, or a combination of these. The light source can be one or more of a halogen lamp, a laser light source, an LED lamp, a mercury lamp, and a xenon lamp. The type and / or wavelength of the light source for fluorescence observation may be multiple and can be selected appropriately by those skilled in the art. The light irradiation unit can have a transmissive, reflective, or incident-light (coaxial incident-light or lateral-light) configuration.

[0034] (Optical Department) The optical unit 5102 is configured to guide light from the biological sample S to the signal acquisition unit 5103. The optical unit can be configured to enable the microscope device 5100 to observe or image the biological sample S. The optical unit 5102 may include an objective lens. The type of objective lens may be appropriately selected by a person skilled in the art depending on the observation method. The optical unit may also include a relay lens for relaying the image magnified by the objective lens to the signal acquisition unit. The optical unit may further include optical components other than the objective lens and the relay lens, such as an eyepiece, a phase plate, and a condenser lens. The optical unit 5102 may further include a wavelength separation unit configured to separate light having a predetermined wavelength from light from the biological sample S. The wavelength separation unit may be configured to selectively allow light of a predetermined wavelength or wavelength range to reach the signal acquisition unit. The wavelength separation unit may include, for example, one or more of a filter that selectively transmits light, a polarizing plate, a prism (Wollaston prism), and a diffraction grating. The optical components included in the wavelength separation unit may be arranged, for example, on the optical path from the objective lens to the signal acquisition unit. The wavelength separation unit is provided in the microscope device when fluorescence observation is performed, particularly when an excitation light irradiation unit is included. The wavelength separation unit may be configured to separate fluorescent light from each other or to separate white light from fluorescent light.

[0035] (Signal acquisition section) The signal acquisition unit 5103 may be configured to receive light from the biological sample S and convert the light into an electrical signal, particularly a digital electrical signal. The signal acquisition unit may be configured to acquire data related to the biological sample S based on the electrical signal. The signal acquisition unit may be configured to acquire data on an image (particularly a still image, a time-lapse image, or a moving image) of the biological sample S, particularly data on an image enlarged by the optical unit. The signal acquisition unit includes one or more imaging elements, such as a CMOS or CCD, having a plurality of pixels arranged one-dimensionally or two-dimensionally. The signal acquisition unit may include an imaging element for acquiring low-resolution images and an imaging element for acquiring high-resolution images, or may include an imaging element for sensing (e.g., AF) and an imaging element for outputting images (e.g., observation). In addition to the plurality of pixels, the imaging element may include a signal processing unit (including one, two, or three of a CPU, a DSP, and a memory) that performs signal processing using pixel signals from each pixel, and an output control unit that controls the output of image data generated from the pixel signals and processed data generated by the signal processing unit. Furthermore, the imaging element may include an asynchronous event detection sensor that detects, as an event, a change in luminance of a pixel that photoelectrically converts incident light exceeding a predetermined threshold. The imaging element including the plurality of pixels, the signal processing unit, and the output control unit may preferably be configured as a single-chip semiconductor device.

[0036] (Control unit) The control unit 5110 controls imaging by the microscope device 5100. For imaging control, the control unit can adjust the positional relationship between the optical unit and the sample mounting unit by driving the movement of the optical unit 5102 and / or the sample mounting unit 5104. The control unit 5110 can move the optical unit and / or the sample mounting unit in a direction toward or away from each other (for example, in the optical axis direction of the objective lens). The control unit may also move the optical unit and / or the sample mounting unit in any direction in a plane perpendicular to the optical axis direction. For imaging control, the control unit may control the light irradiation unit 5101 and / or the signal acquisition unit 5103.

[0037] (Sample placement section) The sample mounting unit 5104 may be configured so that the position of the biological sample on the sample mounting unit can be fixed, and may be a so-called stage. The sample mounting unit 5104 may be configured so that the position of the biological sample can be moved in the optical axis direction of the objective lens and / or in a direction perpendicular to the optical axis direction.

[0038] (Information Processing Department) The information processing unit 5120 may acquire data (such as imaging data) acquired by the microscope device 5100 from the microscope device 5100. The information processing unit may perform image processing on the imaging data. The image processing may include color separation processing. The color separation processing may include processing for extracting data on light components of a predetermined wavelength or wavelength range from the imaging data to generate image data, or processing for removing data on light components of a predetermined wavelength or wavelength range from the imaging data. The image processing may also include autofluorescence separation processing for separating autofluorescence components and pigment components of tissue slices, or fluorescence separation processing for separating wavelengths between pigments with different fluorescence wavelengths. The autofluorescence separation processing may involve using an autofluorescence signal extracted from one of the multiple specimens that are identical or have similar properties to remove the autofluorescence component from image information of the other specimen. The information processing unit 5120 may transmit data for imaging control to the control unit 5110, and the control unit 5110 may receive the data and control imaging by the microscope device 5100 in accordance with the data.

[0039] The information processing unit 5120 may be configured as an information processing device such as a general-purpose computer, and may include a CPU, RAM, and ROM. The information processing unit may be included in the housing of the microscope device 5100, or may be located outside the housing. Furthermore, various processes or functions performed by the information processing unit may be realized by a server computer or cloud connected via a network.

[0040] The method of imaging the biological sample S using the microscope device 5100 may be appropriately selected by those skilled in the art depending on the type of biological sample, the purpose of imaging, etc. Examples of the imaging method will be described below.

[0041] One example of an imaging method is as follows: The microscope device may first identify an imaging target region. The imaging target region may be identified so as to cover the entire region in which the biological sample is present, or may be identified so as to cover a target portion of the biological sample (a portion in which a target tissue slice, a target cell, or a target lesion is present). Next, the microscope device divides the imaging target region into a plurality of divided regions of a predetermined size, and the microscope device sequentially images each divided region. In this way, an image of each divided region is acquired. As shown in FIG. 1, the microscope device identifies an imaging target region R that covers the entire biological sample S. The microscope device then divides the imaging target region R into 16 divided regions. The microscope device then images divided region R1, and may then image any region included in the imaging target region R, such as a region adjacent to divided region R1. The microscope device then images the divided regions until there are no more divided regions left to be imaged. Note that regions other than the imaging target region R may also be imaged based on the captured image information of the divided regions. After imaging a divided region, the positional relationship between the microscope device and the sample mounting unit is adjusted to image the next divided region. This adjustment may be performed by moving the microscope device, the sample mounting unit, or both. In this example, the imaging device that images each divided region may be a two-dimensional imaging element (area sensor) or a one-dimensional imaging element (line sensor). The signal acquisition unit may image each divided region via the optical unit. Furthermore, imaging of each divided region may be performed continuously while moving the microscope device and / or the sample mounting unit, or the movement of the microscope device and / or the sample mounting unit may be stopped when imaging each divided region. The imaging target region may be divided so that each divided region partially overlaps, or so that each divided region does not overlap. Each divided region may be imaged multiple times by changing imaging conditions such as focal length and / or exposure time. The information processing device can also generate image data of a larger area by combining multiple adjacent divided areas. By performing this combining process over the entire imaging target area, an image of a larger area can be obtained for the imaging target area. Furthermore, image data with a lower resolution can be generated from the images of the divided areas or the combined image.

[0042] Another example of an imaging method is as follows: The microscope device may first identify an imaging target area. The imaging target area may be identified so as to cover the entire area where the biological sample is present, or may be identified so as to cover a target portion of the biological sample (a portion where a target tissue section or target cells are present). Next, the microscope device scans and images a portion of the imaging target area (also referred to as a "divided scan area") in one direction (also referred to as the "scan direction") in a plane perpendicular to the optical axis. Once scanning of the divided scan area is complete, the microscope device then scans the divided scan area adjacent to the scanned area. These scanning operations are repeated until the entire imaging target area is imaged. As shown in FIG. 3, the microscope device identifies an area (gray portion) of the biological sample S where a tissue slice exists as the imaging target area Sa. Then, the microscope device scans a divided scan area Rs of the imaging target area Sa in the Y-axis direction. After completing scanning of the divided scan area Rs, the microscope device then scans the adjacent divided scan area in the X-axis direction. This operation is repeated until scanning of the entire imaging target area Sa is completed. The positional relationship between the microscope device and the sample placement unit is adjusted for scanning each divided scan area and for imaging the next divided scan area after imaging one divided scan area. This adjustment may be performed by moving the microscope device, the sample placement unit, or both. In this example, the imaging device that images each divided scan area may be a one-dimensional imaging element (line sensor) or a two-dimensional imaging element (area sensor). The signal acquisition unit may image each divided scan area via a magnifying optical system. Furthermore, imaging of each divided scan area may be performed continuously while moving the microscope device and / or the sample placement unit. The imaging target area may be divided so that the divided scan areas partially overlap, or so that they do not overlap. Each divided scan area may be imaged multiple times by changing imaging conditions such as focal length and / or exposure time. The information processing device can also generate image data of a larger area by combining multiple adjacent divided scan areas. By performing this combining process across the entire imaging target area, an image of a larger area can be acquired for the imaging target area. Furthermore, image data with a lower resolution can be generated from the images of the divided scan areas or from the combined image.

[0043] Fig. 4 is a block diagram showing a more detailed example configuration of the information processing unit 5120. As shown in Fig. 4, the information processing unit 5120 includes a storage unit 100, a processing unit 200, an operation unit 300, and a display unit 400. The information processing unit 5120 according to this embodiment corresponds to an information processing device.

[0044] The storage unit 100 may be a storage device such as a nonvolatile semiconductor memory or a hard disk drive. Various control parameters, programs, and the like according to this embodiment are stored in advance in the storage unit 100. The storage unit 100 also includes an input image database 102 and an analyzed ROI database 104.

[0045] The input image database 102 stores digital captured images captured by the microscope device 5100. For example, first region images captured at a plurality of different depths in the optical axis direction of the optical section of the microscope device 5100 are stored in this captured image database 102. Furthermore, WSI images for each depth generated by stitching a plurality of first region images at the same depth are stored. Furthermore, the images may be images within a partial region indicated by annotation data (such as a tumor region indicated by a pathologist or researcher) accompanying each image. The staining properties of these captured images may be HE (Hematoxylin & Eosin) staining, IHC (Immunohistochemistry) staining, or fluorescent staining.

[0046] The analyzed ROI database 104 stores, in a mutually associated manner, ROI region information associated with the captured image stored in the input image database 102, feature amounts calculated based on the image within the ROI region, and parameters associated with these feature amounts. Note that the analyzed ROI database 104 may store only feature amounts calculated based on the image within the ROI region and parameters associated with these feature amounts. Details of the feature amounts will be described later.

[0047] The processing unit 200 includes a central processing unit (Central Processing Unit) and an MPU (Microprocessor), and configures each processing unit by executing a program stored in the storage unit 100. The processing unit 200 analyzes a digital image captured by the microscope device 5100 and generates analysis information. Details of the processing unit 200 will be described later.

[0048] The program used by the processing unit 200 may be stored in the storage unit 100, or may be stored in a storage medium such as a DVD (Digital Versatile Disc), a cloud computer, etc. In addition, the program may be executed in the processing unit 200 by a CPU (Central Processing Unit) or an MPU (Microprocessor) using a RAM (Random Access Memory) or the like as a working area, or may be executed by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0049] The operation unit 300 is configured with, for example, a keyboard, a mouse, etc. This operation unit 300 inputs, to the processing unit 200, instruction signals according to operations by an observer (user), for example, a pathologist.

[0050] The display unit 400 is, for example, a monitor. As will be described later, the display unit 400 displays captured images, data related to analysis, a user interface (UI screen), and the like.

[0051] Here, we will explain the details of the processing unit 200. The processing unit 200 has an acquisition unit 202, a region extraction unit 204, a feature calculation unit 206, a region selection unit 208, a similarity determination processing unit 210, a parameter setting unit 212, an analysis processing unit 214, and a display control unit 216.

[0052] The acquiring unit 202 acquires a first image (target image) of a sample tissue to be analyzed from the input image database 102. Alternatively, the acquiring unit 202 may acquire the target image directly from the microscope device 5100. Alternatively, the acquiring unit 202 may acquire the target image directly from another microscope device, a storage device, or the like, for example, via an in-hospital network.

[0053] The region extraction unit 204 sets an ROI (Region of Interest) for calculating feature quantities within the captured image G100. FIG. 5 is a diagram showing an example of a captured image Im100 and multiple ROIs for calculating feature quantities. As shown in FIG. 5, the region extraction unit 204 extracts a specimen region T100 from the captured image Im100 and sets multiple ROIs of a predetermined size at predetermined intervals within the specimen region T100. The size and intervals of the ROIs are set in advance in the analyzed ROI database 104 according to the feature quantities to be calculated. The shape of the ROI is not limited, and may be, for example, circular. In this embodiment, the feature quantities may be referred to as characteristic information. This characteristic information is a quantification of at least one of various geometric characteristics, including tissue morphology, and various visual characteristics of patterns, including tissue morphology, present in the image, and the characteristic information is information including the feature quantities.

[0054] The feature calculation unit 206 calculates a feature from each of the multiple ROIs set by the region extraction unit 204. For example, the feature may be a brightness value, cell density, nuclear circularity, nuclear perimeter, color information, frequency characteristics, or local features such as SIFT (Scale Invariant Feature Transform), SURF (Speed ​​Up Robust Features), LBP (Local Binary Pattern), AKAZE (Accelerated KAZE), or HOG (Histogram of Oriented Gradients). While these feature values ​​are used in this embodiment, they are not limited to these. For example, feature values ​​output from a neural network (NN) such as a convolutional neural network (CNN) may also be used. Thus, the feature value may be any feature value that extracts image characteristics of a pathological image and is used for classification, identification, and recognition of pathological morphology. For example, manually designed features include color features of specific morphologies (brightness, staining intensity, etc.), shape features (circularity, perimeter, etc.), density, distance from specific morphologies, and local features (AKAZE, SIFT, HOG, SURF, LBP, etc.), as mentioned above. Furthermore, if the data to be analyzed is IHC stained or fluorescently stained, information such as the number of positive and negative cells calculated within the tissue may also be used.

[0055] FIG. 6 is a diagram showing an example of a characteristic information selection user interface U100 for selecting a feature. An observer, such as a pathologist, selects a feature using the operation unit 300. In FIG. 6, the average value of nuclear circularity within the ROI and the average value of nuclear perimeter within the ROI are selected. For simplicity, the following description will be given using an example in which the average value of nuclear circularity within the ROI and the average value of nuclear perimeter within the ROI are used, but this is not limiting. For example, it is also possible to use a number of feature quantities on the order of tens or hundreds as the total feature quantities. The user interface U100 is displayed on the display unit 400 via the display control unit 216.

[0056] The region selection unit 208 selects a representative ROI based on the distribution of feature amounts calculated from the image information in each of the ROIs set by the region extraction unit 204 . 7 is a diagram showing a schematic distribution of the feature amounts of each ROI calculated by the feature amount calculation unit 206. The vertical axis indicates the average value of the nuclear perimeter for each ROI, and the horizontal axis indicates the average value of the nuclear circularity for each ROI.

[0057] The region selection unit 208 clusters the feature amounts and selects a representative ROI for each clustered region. As shown in Fig. 7, the region selection unit 208 performs clustering using, for example, the k-Means method. The number of classifications can be set to any number. For example, the number of clusters may be manually specified by the user, or may be automatically determined by the region selection unit 208 using the elbow method, silhouette analysis, or the like.

[0058] The region selection unit 208 selects a representative ROI from each of the clusters G100, G102, and G104, for example. The region selection unit 208 according to this embodiment selects the ROI closest to the center of gravity of each of the clusters G100, G102, and G104 as the representative ROI, and selects F100, F102, and F104 as the feature quantities of the representative ROI. In this way, statistical processing makes it possible to select the representative ROI and the feature quantities corresponding to the representative ROI. This allows the representative ROI and the feature quantities corresponding to the representative ROI to be selected without relying on the observer's experience, thereby improving the reproducibility of the selectability of the representative ROI and the feature quantities corresponding to the representative ROI.

[0059] The similarity determination processing unit 210 determines the similarity between the feature amount of the ROI corresponding to a predetermined parameter stored in the analyzed ROI database 104 and the feature amount selected by the region selection unit 208. When the similarity determination processing unit 210 determines that the feature amount of the ROI corresponding to the predetermined parameter is similar to the feature amount selected by the region selection unit 208, it supplies the predetermined parameter to the parameter setting unit 212. In this embodiment, the determination of the similarity between feature amounts may be referred to as a comparison of feature amounts.

[0060] More specifically, the similarity determination processor 210 can perform similarity determination based on the feature quantities F100, F102, and F104 corresponding to each representative ROI in the characteristic information space and the distances or spatial vectors between the feature quantities corresponding to each ROI to be compared. The similarity determination processor 210 determines similarity when, for example, the Euclidean distance or Mahalanobis distance, which is a geometric linear distance, is equal to or less than a threshold. Alternatively, the similarity determination processor 210 may determine similarity when, for example, the value of cosine similarity, which indicates the similarity of the orientation of spatial vectors, is equal to or greater than a threshold. Alternatively, after comparing similarities in each characteristic information space as described above, the similarity between sets may be used by determining whether the distance vectors are close as common terms. In this case, the Jaccard coefficient, Dice coefficient, or Simpson coefficient, which are similarity indices between sets, may be used as an index.

[0061] 8 is a diagram showing the positions of the feature quantities F100, F102, and F104 of the representative ROI within a predetermined feature quantity distribution. Fig. 8 shows an example in which the feature quantities F100, F102, and F104 are at or below a predetermined Mahalanobis distance from each feature quantity within the feature quantity distribution. In such a case, the similarity determination processing unit 210 determines that the feature quantities F100, F102, and F104 are similar to the feature quantities of the ROI corresponding to the parameter.

[0062] FIG. 9 is a diagram illustrating the positions of the feature quantities F100, F102, and F104 of the representative ROI within the feature quantity distribution corresponding to different parameters. FIG. 9 illustrates an example in which the feature quantity F104 is at a predetermined Mahalanobis distance or greater from each feature quantity within the feature quantity distribution. For example, if even one of the representative feature quantities is at a predetermined Mahalanobis distance or greater from each feature quantity within the feature quantity distribution, the similarity determination processor 210 determines that the feature quantities F100, F102, and F104 are not similar to the feature quantities of the ROI corresponding to the parameter. In other words, the similarity determination processor 210 determines whether the pathological morphology in the image within the ROI corresponding to the parameter is similar to the pathological morphology in the image of the representative ROI. The display controller 216 may display the processing results shown in FIGS. 5 to 9 on the display unit 400. This allows the observer to objectively understand the processing results.

[0063] When the similarity determination processing unit 210 determines that the feature amounts F100, F102, and F104 are similar to the feature amounts of the ROI corresponding to the parameters, it causes the display unit 400, via the display control unit, to display a display form that indicates to the user that analysis is possible using the parameters currently in use. On the other hand, when the similarity determination processing unit 210 determines that the feature amounts F100, F102, and F104 are not similar to the feature amounts of the ROI corresponding to the parameters, it causes the display unit 400, via the display control unit, to display a display form that prompts the observer (user) to readjust the parameters. Note that the similarity determination processing unit 210 according to this embodiment corresponds to the determination processing unit.

[0064] The parameter setting unit 212 sets the parameters supplied from the similarity determination processing unit 210 in the analysis processing unit 214. The parameter setting unit 212 can also set the parameters set via the operation unit 300 in the analysis processing unit 214.

[0065] The analysis processing unit 214 analyzes the target image using the parameters set by the parameter setting unit 212. For example, the analysis processing unit 214 performs analysis processing tailored to each case using the set parameters. The analysis processing according to this embodiment may, for example, perform processing to extract a lesion from a pathology image that is the target image, but is not limited to this. For example, the analysis processing may also perform processing to extract a target structure such as a cell nucleus from a pathology image that is the target image. Furthermore, for example, the analysis processing unit 214 may configure multiple image processing filters according to the set parameters and perform analysis processing such as extracting a lesion by combining these multiple image processing filters. Alternatively, processing including a combination of frequency processing and gradation conversion processing may be performed according to the set parameters.

[0066] The display control unit 216 performs processing to display various images on the display unit 400. The display control unit 216 can also read a predetermined UI screen stored in the storage unit 100 and display it on the display unit 400. In this case, the display control unit 216 supplies an instruction signal input via the UI screen to each unit of the processing unit 200.

[0067] Fig. 10 is a flowchart showing an example of processing by the information processing device 5120 according to the first embodiment. As shown in Fig. 10, the acquisition unit 202 acquires a target image to be processed from the input image database 102 (step S100).

[0068] Next, the region extraction unit 204 sets an ROI for calculating features within the captured image G100, the feature calculation unit 206 calculates features from each of the multiple ROIs set by the region extraction unit 204, and the region selection unit 208 selects, for example, a representative ROI from each of the clusters G100, G102, and G104 (step S102).

[0069] Next, the similarity determination processing unit 210 determines the similarity between the feature of the ROI corresponding to the predetermined parameter stored in the analyzed ROI database 104 and the feature selected by the region selection unit 208 (step S106). When the similarity determination processing unit 210 determines that they are similar (y in step S106), it causes the display unit 400 to display a display form via the display control unit that indicates to the user that analysis is possible using the predetermined parameter that was adjusted in the past (step S108).

[0070] On the other hand, if the similarity determination processing unit 210 determines that the images are not similar (n in step S106), it causes the display unit 400, via the display control unit 216, to display a display form that prompts the observer to readjust the parameters (step S110), and terminates the overall processing.

[0071] As described above, the acquisition unit 202 acquires an image of a sample tissue, and the analysis processing unit 214 analyzes the image using predetermined parameters. In this case, the similarity determination processing unit 210 determines whether to perform analysis processing using the predetermined parameters based on characteristic information of tissue morphology in the image within a ROI present in the image and second characteristic information of tissue morphology in the image corresponding to the predetermined parameters. This makes it possible to objectively determine whether analysis processing can be performed using the predetermined parameters on the image acquired by the acquisition unit 202. By extracting an ROI from the image and performing similarity determination processing of characteristic information based on the ROI in this way, it becomes possible to objectively determine whether previously adjusted parameters can be applied to a new image to be analyzed, without relying on experience or knowledge.

[0072] (Second embodiment) The microscope system 5000 according to the second embodiment differs from the microscope system 5000 according to the first embodiment in that the processing unit 200 further includes a parameter adjustment control unit 218. The differences from the microscope system 5000 according to the first embodiment will be described below.

[0073] 11 is a block diagram showing an example of the configuration of an information processing unit 5120 according to the second embodiment. As shown in FIG.

[0074] The parameter adjustment control unit 218 is capable of performing control processing when adjusting parameters. 12 and 13 are diagrams showing example screens displayed on the display unit 400 when the parameter adjustment control unit 218 performs parameter adjustment control via the display control unit 216. Fig. 12 is a diagram showing example screen regions. As shown in Fig. 12, the screen region W110 is a region for displaying an image within the ROI selected by the region selection unit 208, the screen region W112 is a region for displaying information related to the WSI image, the screen region W114 is a region for displaying information related to characteristic information, and the screen region W116 is a region for displaying information related to the similarity determination result.

[0075] Fig. 13 is a diagram showing an example in which specific information is displayed in each region shown in Fig. 12. As shown in Fig. 13, the parameter adjustment control unit 218 causes the display unit 400 to display, in the screen region W110, images in each of the representative ROIs 1 to 7 selected by the region selection unit 208 via the display control unit 216. This allows the observer to confirm the pathological morphology in the image in the representative ROI while comparing it with the pathological morphology in the images in the other representative ROIs.

[0076] Furthermore, the parameter adjustment control unit 218 displays the pathological image W112a, which is the target image, in the screen region W112, and also illustrates the position of the representative ROI. Similarly, the parameter adjustment control unit 218 displays the result image W112b of the clustering performed by the region selection unit 208 in the screen region W112. In the result image W112b, regions with similar characteristic information are visualized by displaying them in the same color, for example. This makes it possible to provide feedback to the user about the correspondence between the characteristic information and the visual characteristics. This feedback allows the user to monitor, for example, the appropriateness of the number of cluster divisions.

[0077] Similarly, the parameter adjustment control unit 218 causes the display unit 400 to display the UI screen U100 and a distribution image W114a of the feature amounts of each ROI read from the storage unit 100 by the similarity determination processing unit 210 in the screen area W114 via the display control unit 216. Furthermore, the ROIs 6 determined to be dissimilar by the similarity determination processing unit 210 are also displayed in the distribution image W114a. This allows the statistical positional relationship of the ROIs 6 determined to be dissimilar to be confirmed on the distribution map of the feature amounts. Therefore, the observer can objectively grasp the statistical positioning of the ROIs 6 determined to be dissimilar.

[0078] Similarly, the parameter adjustment control unit 218 causes the display unit 400 to display an enlarged view 116a of the image in the ROI 6 determined to be dissimilar in the screen area W116 via the display control unit 216. This allows the observer to check the pathological morphology of the ROI 6 determined to be dissimilar in more detail, and to understand the cause of the distribution deviation in the statistical distribution of the feature amount.

[0079] Furthermore, the parameter adjustment control unit 218 displays a display form W116b that prompts the observer to readjust the parameters. The display form W116b is used to input a signal (y or n) indicating whether or not to accept the parameter readjustment.

[0080] This display format allows the observer to objectively grasp the information of the ROI 6 that has been determined to be dissimilar by the similarity determination processing unit 210. In this way, by adding a mechanism for recommending ROIs that are dissimilar to the analyzed ROI, it becomes possible to select an ROI suitable for parameter adjustment without relying on the user's experience or pathological knowledge.

[0081] Furthermore, if the similarity determination processing unit 210 detects an ROI 6 that is dissimilar to the analyzed ROI, it means that the detected ROI 6 contains characteristic information that has not been analyzed before. In other words, this indicates that the recommended ROI 6 is more appropriate as an ROI for new parameter adjustment. In this way, if the characteristic information of an ROI is different from that of the analyzed ROI, it is considered that parameter adjustment is highly useful.

[0082] Fig. 14 is a flowchart showing an example of processing by the information processing device 5120 according to the second embodiment. As shown in Fig. 14, when the similarity determination processing unit 210 determines that there is no similarity (n in step S106), the parameter adjustment control unit 218 causes the display unit 400 to display information about the ROI 6 recommended for parameter readjustment as, for example, the screen shown in Fig. 12 via the display control unit 216 (step S200).

[0083] Next, the observer supplies new parameters to the parameter setting unit 212 via the operation unit 300 (step S202). Then, the parameter adjustment control unit 218 associates the new parameters supplied via the operation unit 300 and the parameter setting unit 212 with the feature quantities of the corresponding ROI 6, stores them in the analyzed ROI database 104, and ends the overall processing.

[0084] Fig. 15 is a flowchart showing an example of processing by the information processing device 5120 when the recommended ROI 6 is not adopted (when not used for parameter readjustment). As shown in Fig. 15, when the similarity determination processing unit 210 determines that the ROIs are not similar (n in step S106), the parameter adjustment control unit 218 further displays a distribution image W114a of the feature amounts of each ROI read from the storage unit 100 by the similarity determination processing unit 210 (step S300). Then, the parameter adjustment control unit 218 determines whether a signal indicating that the recommended ROI 6 is not selected is input from the operation unit 300 via the display form W116b (see Fig. 13) (step S302).

[0085] When a signal (n) indicating that the recommended ROI 6 is not selected is input (y in step S302), the parameter setting unit 212 sets parameters related to any ROI within the analysis target in the analysis processing unit 214 and performs analysis processing.

[0086] Next, the parameter adjustment control unit 218 feeds back imaging information related to the recommended ROI6 to the microscope device 5100 (step S304). For example, imaging conditions that prevent feature amounts related to the recommended ROI6 from being acquired are fed back to the microscope device 5100 (step S306). For example, the parameter adjustment control unit 218 supplies information to the microscope device 5100 for changing the wavelength band and illuminance of the light source of the microscope device 5100. This enables the microscope device 5100 to adjust the wavelength and illuminance of the light source in the light irradiation unit 5101 so as to suppress the acquisition of statistical information related to the recommended ROI6, for example.

[0087] As described above, in this embodiment, the parameter adjustment control unit 218 displays recommended ROIs that are dissimilar to the analyzed ROI on the display unit 400 via the display control unit 216. This makes it possible to select an ROI suitable for parameter adjustment without relying on the user's experience or pathological knowledge.

[0088] (Third embodiment) The microscope system 5000 according to the third embodiment differs from the microscope system 5000 according to the second embodiment in that the processing unit 200 further includes a criterion learning unit 220. The differences from the microscope system 5000 according to the second embodiment will be described below.

[0089] 16 is a block diagram showing an example of the configuration of an information processing device 5120 according to the third embodiment. As shown in FIG.

[0090] As described above, the similarity determination processor 210 prompts parameter adjustment when the representative ROI 6 (see FIG. 13 ) is located outside the existing feature distribution, i.e., when the feature based on the representative ROI 6 is not within a predetermined distance from each feature. On the other hand, there are cases where the representative ROI 6 is not adopted for parameter adjustment. In such cases, unless the position of the feature of the representative ROI 6 in the feature space is brought closer to the existing feature distribution, there is a high possibility that a feature similar to the representative ROI 6 will be adopted for parameter adjustment. Therefore, the determination criterion learning unit 220 performs a process of re-learning the similarity determination criterion so that the feature of the representative ROI 6 approaches the existing feature distribution in the feature space.

[0091] The criterion learning unit 220 re-learns the similarity determination criteria of the similarity determination processing unit 210, for example. As a re-learning method, the criterion learning unit 220 automatically updates the Mahalanobis distance and the threshold value for the space vector used by the similarity determination processing unit 210. This makes it possible to update the threshold value so that the statistics of the rejected representative ROI 6 are determined to be "similar" to the analyzed ROI. Furthermore, by using deep metric learning or the like, learning may be performed so that the rejected representative ROI 6 is plotted in a metric space that generates the feature values ​​of the rejected representative ROI 6 in a position close to the analyzed ROI. In this way, the user's pathological knowledge, etc., can be incorporated into the similarity determination to control the similarity determination criteria of the system.

[0092] Fig. 17 is a flowchart showing an example of re-learning of the information processing device 5120 when not adopting the recommended ROI 6. As shown in Fig. 17, when a signal indicating that the recommended ROI 6 is not selected is input (y in step S302), the criterion learning unit 220 causes, for example, the similarity determination criterion of the similarity determination processing unit 210 to be re-learned (step S400).

[0093] As described above, according to this embodiment, when the similarity determination processor 210 determines that the representative ROI 6 (see FIG. 13 ) is located outside the existing feature distribution and the user does not use the representative ROI 6 for parameter adjustment, the determination criterion learning unit 220 performs processing to re-learn the similarity determination criterion so that the feature of the representative ROI 6 approaches the existing feature distribution in the feature space. This prevents the similarity determination processor 210 from selecting an ROI having a feature similar to the representative ROI 6 as a recommended ROI. In this way, it becomes possible to incorporate pathological knowledge and the like possessed by the user in addition to characteristic information into the similarity determination, and it is also possible to control the similarity determination criterion of the system.

[0094] (Fourth embodiment) The microscope system 5000 according to the fourth embodiment differs from the microscope system 5000 according to the second embodiment in that, when the similarity determination processing unit 210 determines that the representative ROI 6 (see FIG. 13) is located in an outlying position in the existing feature amount distribution, it determines whether there is similar information in the characteristic information stored in the storage unit 100. The differences from the microscope system 5000 according to the second embodiment will be described below.

[0095] 18 is a flowchart showing an example of determining whether similar information exists in the characteristic information stored in the storage unit 100. As shown in FIG. 18, when the similarity determination processing unit 210 determines that there is no similarity (n in step S106), the parameter adjustment control unit 218 determines whether a feature corresponding to the recommended ROI 6 exists in the storage unit 100 (step S500). When the parameter adjustment control unit 218 determines that a similar feature exists (y in step S500), the parameter adjustment control unit 218 causes the display unit 400 to display the parameters associated with the similar feature and the image in the ROI associated with the parameters via the display control unit 216 (step S502). Then, a display form is displayed that recommends analysis using the displayed parameters to the user.

[0096] On the other hand, if no determination is made (n in step S500), the above steps S200 to S204 are executed.

[0097] As described above, according to this embodiment, when the similarity determination processing unit 210 determines that the representative ROI 6 (see FIG. 13) is not similar in the existing feature distribution, it determines whether or not a feature corresponding to the recommended ROI 6 exists in the storage unit 100. This makes it possible to use parameters associated with the corresponding feature when a feature corresponding to the recommended ROI 6 exists in the storage unit 100, thereby making it possible to further improve the efficiency of processing.

[0098] (Modification of the fourth embodiment) The microscope system 5000 according to the fifth embodiment differs from the microscope system 5000 according to the fourth embodiment in that the determination distance used in the similarity determination processing unit 210 is changed according to the number of pieces of characteristic information stored in the storage unit 100. The differences from the microscope system 5000 according to the fourth embodiment will be described below.

[0099] Fig. 19 is a diagram showing an example of a judgment distance used by the similarity judgment processing unit 210. The horizontal axis indicates the number of pieces of characteristic information stored in the storage unit 100, and the vertical axis indicates the judgment distance used by the similarity judgment processing unit 210. As shown in Fig. 19, the similarity judgment processing unit 210 shortens the predetermined Mahalanobis distance used for judgment as the amount of characteristic information associated with the parameter increases.

[0100] The number of characteristic information items associated with new parameters increases as the number of captured images increases. Therefore, by shortening the predetermined Mahalanobis distance used for judgment as the number of characteristic information items increases, it becomes possible to stabilize the extent of the judgment area of ​​the similarity of the representative ROI. This makes it possible to suppress the judgment processing accuracy of the similarity judgment processing unit 210 despite fluctuations in the number of data items of characteristic information.

[0101] The present technology can be configured as follows:

[0102] (1) an acquisition step of acquiring a first image of the sample tissue; an analysis processing step of analyzing the first image using predetermined parameters; a determination step of determining whether to perform the analysis process using the predetermined parameters based on first characteristic information of tissue morphology present in the first image and second characteristic information of tissue morphology corresponding to the predetermined parameters; An information processing program that causes a computer to execute the above.

[0103] (2) The information processing program described in (1), wherein the judgment process determines not to perform the analysis process using the specified parameters if the first characteristic information and the second characteristic information are not within a specified distance on the statistical distribution.

[0104] (3) The information processing program described in (2) further includes a display control step of, when the judgment step determines that the analysis process should not be performed using the specified parameters, displaying on the display unit a display format that recommends setting new parameters for the analysis processing process.

[0105] (4) the first characteristic information is generated based on an image within a predetermined region of the first image, The information processing program according to (3), wherein the display format is at least one of an image within the predetermined area and an image showing the predetermined area.

[0106] (5) The information processing program according to (3), wherein the display format displays language relating to a recommendation to set parameters.

[0107] (6) The information processing program described in any one of (3) to (5), wherein the display control process displays an image showing the position information of the first characteristic information within the statistical distribution and the display form side by side on the display unit.

[0108] (7) the first characteristic information is a plurality of pieces of characteristic information selected from a plurality of pieces of characteristic information based on a plurality of processing regions in the first image, The information processing program according to any one of (3) to (6), wherein the display control step causes images within the processing area corresponding to the selected plurality of pieces of characteristic information to be displayed side by side on the display unit.

[0109] (8) the first characteristic information is a plurality of pieces of characteristic information selected from each clustering area by clustering a plurality of pieces of characteristic information based on each of a plurality of processing areas in the first image into a plurality of areas, The information processing program according to any one of (3) to (6), wherein the display control step displays the processing area for each of the clustered areas in association with each other on the first image.

[0110] (9) the first characteristic information is a plurality of pieces of characteristic information selected from a plurality of pieces of characteristic information based on a plurality of processing regions in the first image, The information processing program according to any one of (1) to (6), wherein the judgment step determines that the analysis process should not be performed using the specified parameters, and changes the judgment criteria so that the first characteristic information is not selected when parameters based on the processing area corresponding to the first characteristic information are not used in the analysis process.

[0111] (10) the first characteristic information is a plurality of pieces of characteristic information selected from a plurality of pieces of characteristic information based on a plurality of processing regions in the first image, The information processing program according to any one of (1) to (6), wherein the determination step determines that the analysis process should not be performed using the specified parameters, and when parameters based on a processing area corresponding to the first characteristic information are not used in the analysis process, the imaging conditions of the imaging device that captured the first image are changed so that the first characteristic information is not selected.

[0112] (11) The information processing program according to any one of (2) to (10), wherein the second characteristic information is a plurality of pieces of characteristic information calculated from different captured images.

[0113] (12) The information processing program according to (9), wherein the determining step changes the predetermined distance depending on the number of pieces of second characteristic information corresponding to the predetermined parameter.

[0114] (13) a storage step of storing a plurality of parameters used in the analysis processing step in a storage unit in association with the corresponding second characteristic information; An information processing program according to any one of (1) to (12), wherein, when the judgment process determines that the analysis process should not be performed using the specified parameters, new parameters for the analysis processing process corresponding to the first characteristic information are stored in the memory unit in association with the first characteristic information.

[0115] (14) The information processing program described in (13), wherein the judgment process, when it is determined that the analysis process should not be performed using the specified parameters, determines whether characteristic information similar to the first characteristic information is stored in the memory unit, and if not, stores new parameters for the analysis processing process corresponding to the first characteristic information in the memory unit.

[0116] (15) The information processing program described in (14), wherein the judgment process, when it is determined that characteristic information similar to the first characteristic information is stored in the memory unit, causes the analysis process to be performed based on parameters corresponding to the similar characteristic information.

[0117] (16) The information processing program according to (1), wherein the determination step causes the analysis process to be performed using the predetermined parameters when the first characteristic information and the second characteristic information are within a predetermined distance on a statistical distribution.

[0118] (17) The information processing program described in (1), further comprising a display control step of, when the determination step determines that the analysis processing is to be performed using the specified parameters, displaying on a display unit a display format indicating that the analysis processing is possible using the specified parameters.

[0119] (18) The information processing program according to (2), wherein the first characteristic information and the second characteristic information are at least one of a brightness value, a cell density, a cell circularity, a cell perimeter, and a local feature, and the distance is at least one of a Mahalanobis distance and a Euclidean distance.

[0120] (19) an acquisition unit that acquires a first image of the sample tissue; an analysis processing unit that analyzes and processes the first image using predetermined parameters; a determination unit that determines whether to perform the analysis process using the predetermined parameters based on first characteristic information of tissue morphology present in the first image and second characteristic information of tissue morphology corresponding to the predetermined parameters; An information processing device comprising:

[0121] (20) an acquisition step of acquiring a first image of the sample tissue; an analysis processing step of analyzing the first image using predetermined parameters; a determination step of determining whether to perform the analysis process using the predetermined parameters based on first characteristic information of tissue morphology present in the first image and second characteristic information of tissue morphology corresponding to the predetermined parameters; An information processing method comprising:

[0122] (twenty one) a microscope device that acquires a first image of a sample tissue; An information processing device, The information processing device includes: an acquisition unit that acquires a first image of the sample tissue; an analysis processing unit that analyzes and processes the first image using predetermined parameters; a determination unit that determines whether to perform the analysis process using the predetermined parameters based on first characteristic information of tissue morphology present in the first image and second characteristic information of tissue morphology corresponding to the predetermined parameters; A microscope system comprising:

[0123] The aspects of the present disclosure are not limited to the individual embodiments described above, but include various modifications that may be conceived by those skilled in the art, and the effects of the present disclosure are not limited to the above-described contents. In other words, various additions, modifications, and partial deletions are possible within the scope of the conceptual idea and spirit of the present disclosure, which is derived from the contents defined in the claims and their equivalents. [Explanation of symbols]

[0124] 100: memory unit, 200: processing unit, 202: acquisition unit, 204: area extraction unit, 206: feature calculation unit, 208: area selection unit, 210: similarity determination processing unit, 212: parameter setting unit, 214: analysis processing unit, 216: display control unit, 5000: microscope system, 400: display unit, 5120: information processing device, 5100: microscope device.

Claims

1. an acquiring step of acquiring a first image of the sample tissue; an analysis processing step of analyzing the first image using predetermined parameters; a determination step of determining whether to perform the analysis process using the predetermined parameters based on first characteristic information of tissue morphology present in the first image and second characteristic information of tissue morphology corresponding to the predetermined parameters; a display control step of displaying on a display unit a display format that recommends setting new parameters for the analysis processing step when the determination step determines that the analysis processing should not be performed using the predetermined parameters, the first characteristic information is a plurality of pieces of characteristic information selected from each clustering area by clustering a plurality of pieces of characteristic information based on each of a plurality of processing areas in the first image into a plurality of areas, the display control step displays the processing area for each of the clustered areas in association with each other on the first image. An information processing program to be executed by a computer.

2. 2. The information processing program according to claim 1, wherein the determining step determines not to perform the analysis process using the predetermined parameters when the first characteristic information and the second characteristic information are not within a predetermined distance on a statistical distribution.

3. the first characteristic information is generated based on an image within a predetermined region of the first image, The information processing program according to claim 2 , wherein the display mode is at least one of an image within the predetermined area and an image showing the predetermined area.

4. 3. The information processing program according to claim 2, wherein the display mode displays language relating to a recommendation to set a parameter.

5. The information processing program according to claim 2 , wherein the display control step causes the display unit to display an image indicating position information of the first characteristic information within the statistical distribution and the display format side by side.

6. the first characteristic information is a plurality of pieces of characteristic information selected from a plurality of pieces of characteristic information based on a plurality of processing regions in the first image, The information processing program according to claim 2 , wherein the display control step causes the display unit to display images in the processing area corresponding to the selected plurality of pieces of characteristic information in a row.

7. the first characteristic information is a plurality of pieces of characteristic information selected from a plurality of pieces of characteristic information based on a plurality of processing regions in the first image, 2. The information processing program according to claim 1, wherein the determination step determines that the analysis process should not be performed using the specified parameters, and changes the determination criteria so that the first characteristic information is not selected when parameters based on a processing area corresponding to the first characteristic information are not used in the analysis process.

8. the first characteristic information is a plurality of pieces of characteristic information selected from a plurality of pieces of characteristic information based on a plurality of processing regions in the first image, 2. The information processing program according to claim 1, wherein the determination step changes the imaging conditions of the imaging device that captured the first image so that the first characteristic information is not selected when it is determined that the analysis process will not be performed using the specified parameters and when parameters based on a processing area corresponding to the first characteristic information are not used in the analysis process.

9. The information processing program according to claim 2 , wherein the second characteristic information is a plurality of pieces of characteristic information calculated from different captured images.

10. The information processing program according to claim 7 , wherein the determining step changes the predetermined distance depending on the number of the second characteristic information corresponding to the predetermined parameter.

11. An acquisition step of acquiring a first image of a sample tissue; an analysis processing step of analyzing the first image using predetermined parameters; a determination step of determining whether to perform the analysis process using the predetermined parameters based on first characteristic information of tissue morphology present in the first image and second characteristic information of tissue morphology corresponding to the predetermined parameters; a storage step of storing a plurality of parameters used in the analysis processing step in a storage unit in association with the corresponding second characteristic information, when the determination step determines that the analysis process should not be performed using the predetermined parameters, new parameters for the analysis process corresponding to the first characteristic information are stored in the storage unit in association with the first characteristic information; the determining step further determines whether or not characteristic information similar to the first characteristic information is stored in the storage unit, and if not, stores new parameters for the analysis processing step corresponding to the first characteristic information in the storage unit. An information processing program to be executed by a computer.

12. The information processing program according to claim 11, wherein the determination step, when it is determined that characteristic information similar to the first characteristic information is stored in the storage unit, causes the analysis process to be performed based on parameters corresponding to the similar characteristic information.

13. 2. The information processing program according to claim 1, wherein the determining step causes the analysis process to be performed using the predetermined parameters when the first characteristic information and the second characteristic information are within a predetermined distance on a statistical distribution.

14. The information processing program described in Claim 1, wherein the display control process, when the judgment process determines that the analysis process should be performed using the specified parameters, causes the display unit to display a display format indicating that analysis processing is possible using the specified parameters.

15. 3. The information processing program according to claim 2, wherein the first characteristic information and the second characteristic information are at least one of a luminance value, a cell density, a nuclear circularity, a nuclear perimeter, color information, a frequency characteristic, and a local feature amount, and the distance is at least one of a Mahalanobis distance and a Euclidean distance.

16. an acquisition unit that acquires a first image of the sample tissue; an analysis processing unit that analyzes the first image using predetermined parameters; a determination unit that determines whether to perform the analysis process using the predetermined parameters based on first characteristic information of tissue morphology present in the first image and second characteristic information of tissue morphology corresponding to the predetermined parameters; a display control unit that, when the determination unit determines that the analysis processing should not be performed using the predetermined parameters, causes a display unit to display a display format that recommends setting new parameters to the analysis processing unit; the first characteristic information is a plurality of pieces of characteristic information selected from each clustering area by clustering a plurality of pieces of characteristic information based on each of a plurality of processing areas in the first image into a plurality of areas, The display control unit displays the processing area for each of the clustered areas in association with each other on the first image.

17. an acquiring step of acquiring a first image of the sample tissue; an analysis processing step of analyzing the first image using predetermined parameters; a determination step of determining whether to perform the analysis process using the predetermined parameters based on first characteristic information of tissue morphology present in the first image and second characteristic information of tissue morphology corresponding to the predetermined parameters; a display control step of displaying on a display unit a display format that recommends setting new parameters for the analysis processing step when the determination step determines that the analysis processing should not be performed using the predetermined parameters, the first characteristic information is a plurality of pieces of characteristic information selected from each clustering area by clustering a plurality of pieces of characteristic information based on each of a plurality of processing areas in the first image into a plurality of areas, The information processing method, wherein the display control step displays the processing area for each of the clustered areas in association with each other on the first image.

18. a microscope device that acquires a first image of a sample tissue; An information processing device, The information processing device includes: an acquisition unit that acquires a first image of the sample tissue; an analysis processing unit that analyzes the first image using predetermined parameters; a determination unit that determines whether to perform the analysis process using the predetermined parameters based on first characteristic information of tissue morphology present in the first image and second characteristic information of tissue morphology corresponding to the predetermined parameters; a display control unit that, when the determination unit determines that the analysis processing should not be performed using the predetermined parameters, causes a display unit to display a display format that recommends setting new parameters to the analysis processing unit; and and the first characteristic information is a plurality of pieces of characteristic information selected from each clustering area by clustering a plurality of pieces of characteristic information based on each of a plurality of processing areas in the first image into a plurality of areas, The display control unit displays the processing area for each of the clustered areas in association with each other on the first image.

Images