Image providing device, image providing system, image providing method, and program

Abstract

To provide an image providing device and the like that can provide useful and highly satisfactory image information.SOLUTION: An image providing device 30 comprises: a specific region extraction part 41 which extracts a specific region W from a plurality of cross-sectional images P obtained by imaging an organ of a subject; a shape measurement part 42 which measures the shape of the specific region W; an image classification part 43 which allocates each of the cross-sectional images P to a plurality of groups G1 to 3; an image selection part 44 which selects a representative image D from each of the groups G1 to 3 on the basis of a measured value by the shape measurement part 42; and an image providing part 44 which provides the representative image D.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an image providing device and the like that provides various information to subjects undergoing medical checkups, brain checkups, and the like. [Background technology]

[0002] As people become more health conscious, the number of people undergoing medical checkups aimed at early detection and treatment of illnesses is increasing year by year. Medical checkups offer more detailed and tailored optional tests. One of these is the brain checkup. Cross-sectional images of the brain taken with an MRI or other device are presented as the results of the brain checkup.

[0003] Patent Document 1 discloses a technique for supporting setting of an imaging position in a medical imaging apparatus. Patent Document 2 discloses a medical checkup report viewing system that allows a medical checkup report to be viewed from a remote location. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2012 / 008296 [Patent Document 2] JP 2006-236061 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, the technology of Patent Document 1 is intended to reduce the burden on the operator of the medical imaging device, and is not intended to present a medical examination report to a patient undergoing a medical checkup. Patent Document 2 presents a medical examination report for a medical checkup, but does not describe the specific contents of the medical examination report. There is a demand for providing useful and satisfying medical examination reports to patients undergoing brain checkups. In particular, there is a need for the presentation of cross-sectional images of the brain suitable for brain checkups.

[0006] An object of the present invention is to provide an image providing device and the like that can provide useful and highly satisfying image information to subjects undergoing medical checkups, brain checkups, and the like. [Means for solving the problem]

[0007] In order to solve the above problem, a first embodiment of the image providing device of the present invention comprises a specific area extraction unit that extracts a specific area from a plurality of cross-sectional images of a subject's organs, a shape measurement unit that measures the shape of the specific area, an image division unit that divides each of the cross-sectional images into a plurality of groups, an image selection unit that selects a representative image from each of the groups based on the measurement values ​​by the shape measurement unit, and an image providing unit that provides the representative images.

[0008] In a second embodiment of the image providing apparatus of the present invention, in the first embodiment, the organ is the brain, and the specific region is a white matter hyperintensity area. A third embodiment of the image providing apparatus of the present invention is the first or second embodiment, wherein the organ is a brain, and the specific region is a stroke region.

[0009] A fourth embodiment of the image providing device of the present invention is based on any one of the first to third embodiments, wherein the specific region extraction section extracts the specific region by pattern recognition. A fifth embodiment of the image providing device of the present invention is based on any one of the first to fourth embodiments, wherein the shape measuring section measures an area, a major axis, a minor axis or a perimeter of the specific region. A sixth embodiment of the image providing device of the present invention is, in any of the first to seventh embodiments, wherein the specific area extraction unit obtains a three-dimensional image of the specific area based on positional information in the slice thickness direction of the cross-sectional image and positional information on the slice plane of the specific area, and the shape measurement unit measures the volume of the three-dimensional image.

[0010] A seventh embodiment of the image providing apparatus of the present invention is characterized in that in any one of the first to sixth embodiments, the image partitioning section sets the groups based on position information in the slice thickness direction of the cross-sectional images. An eighth embodiment of the image providing device of the present invention is based on any one of the first to seventh embodiments, wherein the image division section sets the group based on the presence or absence of the specific area. A ninth embodiment of the image providing device of the present invention is based on any one of the first to eighth embodiments, wherein the image partitioning section sets the group based on position information on a slice plane of the specific region. A tenth embodiment of the image providing device of the present invention is based on any one of the first to ninth embodiments, wherein the image division section classifies the specific area into a plurality of areas, and sets the groups based on this classification. An eleventh embodiment of the image providing device of the present invention is any of the first to tenth embodiments, wherein the specific area extraction unit obtains a three-dimensional image of the specific area based on position information in the slice thickness direction of the cross-sectional image and position information on the slice plane of the specific area, and the image division unit sets the group based on the presence or absence of the three-dimensional image. A twelfth embodiment of the image providing apparatus of the present invention is characterized in that in any one of the first to eleventh embodiments, the image partitioning section sets the groups based on position information in the slice thickness direction of the three-dimensional image.

[0011] A thirteenth embodiment of the image providing device of the present invention is, in any of the first to twelfth embodiments, wherein the image selection unit calculates the similarity between the representative images, and if the similarity exceeds a predetermined reference value, reselects the representative image. A fourteenth aspect of the image providing device of the present invention is any of the first to thirteenth aspects, wherein the similarity is found based on position information of the representative image in the slice thickness direction. A fifteenth embodiment of the image providing device of the present invention is characterized in that in any of the first to fourteenth embodiments, the image selection section changes the type of the measurement value and reselects the representative image. A sixteenth embodiment of the image providing device of the present invention is, in any of the first to fifteenth embodiments, wherein the image selection unit selects the representative image based on position information in the slice thickness direction of the cross-sectional image when the specific region is not extracted from the cross-sectional image in the group.

[0012] A first embodiment of the image providing system of the present invention comprises any one of the first to sixteenth embodiments of the image providing device of the present invention, an image capturing device that captures the cross-sectional images, and a PACS server that receives the cross-sectional images from the image capturing device and transfers them to the image providing device, and the image providing device transmits the representative image to the PACS server. A second embodiment of the image providing system of the present invention is the first embodiment, further comprising a terminal device which communicates with the image providing device via an electrical communication network by operation of the subject, and the image providing device transmits the representative image to the terminal device based on instructions from the terminal device.

[0013] An embodiment of the image providing method of the present invention comprises a specific area extraction step of extracting a specific area from a plurality of cross-sectional images of a subject's organs, a shape measurement step of measuring the shape of the specific area, an image division step of sorting each of the cross-sectional images into a plurality of groups, an image selection step of selecting a representative image from each of the groups based on the measurement values ​​obtained in the shape measurement step, and an image providing step of providing the representative image.

[0014] An embodiment of the program of the present invention causes a CPU of an image providing device to function as a specific area extraction unit that extracts a specific area from a plurality of cross-sectional images of a subject's organs, a shape measurement unit that measures the shape of the specific area, an image division unit that divides each of the cross-sectional images into a plurality of groups, an image selection unit that selects a representative image from each of the groups based on the measurement values ​​by the shape measurement unit, and an image providing unit that provides the representative images. Effect of the Invention

[0015] According to the image providing device of the present invention, it is possible to provide useful and highly satisfying image information to subjects undergoing brain checkups and the like. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of a brain image providing system 1. [Diagram 2] 2 is a diagram illustrating an example of a hardware configuration of a brain image providing device 30. FIG. [Diagram 3] 2 is a diagram showing an example of the software configuration of the brain image providing device 30. FIG. [Figure 4] 1 is a diagram showing groups G1 to G3 of axial cross-sectional images P. FIG. [Diagram 5] 1 is a flowchart showing a method for providing a brain image. [Figure 6] FIG. 13 is a diagram showing a presentation image Q. [Figure 7] 1 is a diagram showing a medical checkup report R including a presented image Q. FIG. [Figure 8] This is a diagram showing white matter hyperintensity areas W (hyperintensity areas around the lateral ventricles X, and hyperintensity areas in the deep subcortical regions Y). DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Hereinafter, an image providing device, an image providing system, an image providing method, and a program according to an embodiment of the present invention will be described. As an example, a case will be described in which a useful and satisfying medical checkup report is presented to a patient undergoing a brain checkup.

[0018] [Brain image provision system 1] FIG. 1 is a diagram showing the overall configuration of a brain image providing system 1. As shown in FIG. The brain image providing system (image providing system) 1 includes a brain image capturing device 10, a PACS server 20, and a brain image providing device 30.

[0019] The brain imaging device (imaging device) 10 is, for example, an MRI (Magnetic Resonance Imaging) device or a CT (Computed Tomography) device. The brain imaging device 10 may be a device such as a PET (positron emission tomography) device or a radioisotope imaging device (RI).

[0020] The brain image capturing device 10 captures a cross-sectional image of the brain of a subject (patient, examinee). This cross-sectional image (image P) is also called a slice. The brain image capturing device 10 captures images P of a horizontal section perpendicular to the body axis direction (Axial), a frontal section perpendicular to the sagittal horizontal axis direction (Sagittal), and a sagittal section perpendicular to the frontal horizontal axis direction (Coronal). The slice thickness (thickness of the cross section) and slice gap (the distance between the images P) are set to be constant, for example, 5.0 mm and 1.8 mm. The brain image capturing device 10 captures at least 10 to 20 horizontal slice images, frontal slice images, and sagittal slice images in one examination. A plurality of images P captured by the brain image capturing device 10 are transmitted to the PACS server 20.

[0021] The PACS (Picture Archiving and Communication System) server 20 is a server that stores, browses, manages, etc., a plurality of cross-sectional images captured by the brain image capturing device 10. The PACS server 20 is also called a DICOM server. DICOM (Digital Imaging and Communication in Medicine) is a standard that defines the storage of images and their metadata, and the transfer method (data format) between devices. The PACS server 20 is connected to the brain image capturing apparatus 10 via a LAN (Local Area Network). A plurality of brain image capturing apparatuses 10 may be connected to one PACS server 20. The PACS server 20 stores the multiple images P received from the brain image capturing device 10 and transmits (transfers) them to the brain image providing device 30.

[0022] A plurality of client PCs 15 are connected to the PACS server 20 via a LAN. The client PCs 15 are terminals operated by doctors or the like, and receive images P of the subject's brain from the PACS server 20. The client PCs 15 each have a display, and the images P of the subject's brain are displayed on the display.

[0023] The brain image providing device (image providing device) 30 is connected (interoperates) with the PACS server 20 via a network (electrical communication network) such as a LAN or the Internet. The brain image providing device 30 processes a plurality of images P received from the PACS server 20, and generates brain image information (presented images Q) to be presented to the subject, and a medical checkup report R including the presented images Q.

[0024] A portable information terminal 50 such as a smartphone carried by the subject can be connected to the brain image providing device 30 via the Internet. The subject can operate a portable information terminal (terminal device) 50 to communicate with the brain image providing device 30 and obtain (view, download, etc.) the subject's own brain image information (presented image Q, health check report R) from the brain image providing device 30.

[0025] [Hardware configuration of brain image providing device 30] FIG. 2 is a hardware configuration diagram of the brain image providing device 30. As shown in FIG. The brain image providing device 30 includes a control unit 31 and the like. The control unit 31 includes a CPU 311, a ROM 312, a RAM 313, a HDD 314, and a network I / F 315.

[0026] The CPU 311 reads out various programs (programs of the present invention) stored in the ROM 312 and executes various processes to control the overall operation. The CPU 311 reads out the programs from the ROM 312 and executes the brain image providing method (the image providing method of the present invention). As a result, the CPU 311 makes the brain image providing device 30 function as a specific region extracting unit, a shape measuring unit, an image dividing unit, an image selecting unit, and an image providing unit of the present invention.

[0027] The RAM 313 is used as a temporary storage area such as a main memory or a work area for the CPU 311 . The HDD 314 is a large-capacity storage unit that stores image information and various programs. Cross-sectional images of the subject's brain are stored in the HDD 314. The network I / F 315 is an interface that connects the brain image providing device 30 to a LAN or the Internet. The brain image providing device 30 receives a processing request (instruction) from the mobile information terminal 50 or the like via the network I / F 315, and transmits various information to the mobile information terminal 50 or the like.

[0028] [Software (program) configuration of brain image providing device 30] FIG. 3 is a diagram showing the software configuration of the brain image providing device 30. As shown in FIG. FIG. 4 is a diagram showing groups G1 to G3 of axial cross-sectional images P. As shown in FIG. The brain image providing device 30 comprises a specific region extracting unit 41 , a shape measuring unit 42 , an image dividing unit 43 , an image selecting unit 44 , and an image providing unit 45 .

[0029] The specific region extraction unit 41 extracts white matter hyperintensity regions (specific regions) W from all images P. The specific region extraction unit 41 uses existing technology to extract the white matter hyperintensity regions W. The specific region extraction unit 41 extracts the white matter hyperintensity regions W by, for example, pattern recognition (including deep learning). Information on the presence or absence of white matter hyperintensity areas W is linked to each image P.

[0030] The shape measurement unit 42 measures the shape of the white matter hyperintensity region W extracted by the specific region extraction unit 41. Specifically, the shape measurement unit 42 measures the area of ​​the white matter hyperintensity region W. The shape measurement unit 42 also measures the position of the white matter hyperintensity region W on the slice plane. The shape measurement unit 42 measures the area of ​​white matter hyperintensity regions W for all images P. When multiple white matter hyperintensity regions W exist in one image P, the area of ​​each white matter hyperintensity region W is measured. The total area of ​​the multiple white matter hyperintensity regions W is also measured. Measurements of the shape of white matter hyperintensities W are associated with each image P.

[0031] The image division unit 43 divides (groups) a plurality of cross-sectional images of the brain of the subject into a plurality of groups. Specifically, the image division unit 43 divides horizontal cross-sectional images (a plurality of images P) perpendicular to the body axis direction among the cross-sectional images of the brain of the subject into a plurality of groups. As shown in Fig. 4, the brain is divided into three groups (upper brain layer G1, middle brain layer G2, and lower brain layer G3) along the direction of the body axis. The three groups G1 to G3 are hierarchical layers in the direction of the body axis of the brain. The upper brain layer G1 is a layer (area) that mainly corresponds to the parietal lobe and also to the frontal lobe. The middle brain layer G2 is a layer that mainly corresponds to the frontal lobe and occipital lobe and also to the temporal lobe. The lower brain layer G3 is a layer that mainly corresponds to the temporal lobe and also to the frontal lobe. The images P of the horizontal slices are divided into one of the groups: upper brain layer G1, middle brain layer G2, and lower brain layer G3.

[0032] The image selection unit 44 selects, as a representative image D, the image P having the largest area of ​​white matter hyperintensity region W in each of the groups G1 to G3. In each of the groups G1 to G3, when white matter hyperintensity regions W cannot be extracted from multiple images P, a standard image P for that group is selected. The standard image P can be selected arbitrarily.

[0033] The image providing unit 45 presents the three representative images D selected by the image selecting unit 44, for example, arranged horizontally. The arrangement of the three representative images D can be set arbitrarily. The arrangement of the representative images D may be set based on a hanging protocol. The hanging protocol can be set for each brain image capturing apparatus 10, each subject (patient), each doctor, and each examination site. The image providing unit 45 generates image information (presentation image Q) in which a plurality of representative images D are arranged, and further generates a medical checkup report R including the presentation image Q (see FIGS. 6 and 7).

[0034] The presentation image Q may be one piece of image information or multiple pieces of image information. The presentation image Q may be in any image format (image data) that allows the three representative images D to be displayed at the output (transmission) destination. The health check report R includes, for example, the white matter hyperintensity volume, P / D (near / deep lateral ventricle) score, previous diagnosis information, a graph showing the subject's position in data from the same age group, a graph showing changes over time, and standard text based on the analysis results.

[0035] Moreover, the image providing unit 45 outputs (provides) the presented image Q and the medical examination report R to the outside via a network such as a LAN or the Internet. For example, the image providing unit 45 transmits the presentation image Q to the PACS server 20. As a result, the presentation image Q can be displayed on the display of the client PC 15 connected to the PACS server 20. For example, the image providing unit 45 transmits the medical checkup report R to a portable information terminal 50 carried by the subject via the Internet. This allows the subject to view the medical checkup report R using the portable information terminal 50.

[0036] [Brain image provision method (image provision method)] FIG. 5 is a flow chart showing a brain image providing method. FIG. 6 is a diagram showing a presentation image Q. As shown in FIG. FIG. 7 is a diagram showing a medical checkup report R including a presentation image Q. As shown in FIG. The brain image providing method includes a specific region extracting step S1, a shape measuring step S2, an image dividing step S3, an image selecting step S4, and an image providing step S5. It also includes an image displaying step S6.

[0037] Prior to the brain image providing method, the brain image providing device 30 obtains a plurality of images P from the brain image photographing device 10. It is assumed that the brain image providing device 30 obtains 30 images P (images P1 to P30). The 30 images P are assigned serial numbers from the upper part (top) to the lower part (bottom) in the body axis direction. In addition, position information in the body axis direction (slice thickness direction) is added to each image P. Specifically, image P1 is image data obtained by photographing the most apical part of the brain, and position information of this most apical part is added. Image P30 is image data obtained by photographing the most bottom part of the brain, and position information of this most bottom part is added.

[0038] (Specific area extraction step S1) In the specific region extraction step S1, the specific region extraction unit 41 of the brain image providing device 30 extracts white matter high signal intensity regions (specific regions) W from each image P. The specific region extraction unit 41 extracts the white matter high signal intensity regions W by pattern recognition.

[0039] (Shape measurement process S2) In the shape measurement step S2, the shape measurement unit 42 measures the area of ​​white matter hyperintensity regions W extracted in each image P. When multiple white matter hyperintensity regions W are present in a single image P, the area of ​​each white matter hyperintensity region W is measured, or the total area is measured. Furthermore, the shape measurement unit 42 also measures the positions of the white matter high signal intensity areas W on the slice plane.

[0040] (Image classification step S3) In the image division step S3, the image division unit 43 divides the brain cross-sectional images of the subject into a plurality of groups. Specifically, the image division unit 43 divides horizontal cross-sectional images (images P1 to P30) perpendicular to the body axis direction among the plurality of brain cross-sectional images into a plurality of groups. As described above, three groups (upper brain layer G1, middle brain layer G2, lower brain layer G3) are set along the direction of the body axis of the brain. For example, when there are 30 horizontal cross-sectional images P (images P1 to P30), 10 images are assigned to each of groups G1 to G3. Images P1 to P10 are assigned to the upper brain layer G1, images P11 to P20 to the middle brain layer G2, and images P21 to P30 to the lower brain layer G3.

[0041] (Image selection step S4) In the image selection step S4, the image selection unit 44 selects a representative image D for each group. First, the image selection unit 44 determines whether or not a white matter high signal intensity region W has been detected in each of the three groups G1 to G3 (S41). The image selection unit 44 selects, from the group in which a white matter hyperintensity region W is detected, the image P (representative image D) that has the largest area of ​​the white matter hyperintensity region W (S42). For example, image P4 from the upper brain layer G1, image P16 from the middle brain layer G2, and image P23 from the lower brain layer G3 are selected as representative images D.

[0042] On the other hand, if there is a group in which white matter high signal intensity regions W cannot be detected, the image selection unit 44 selects a standard image P for that group as the representative image D (S43). In that group, the image P located at the center (centre) in the slice thickness direction is the standard image. For example, if no white matter high signal intensity region W can be extracted from the images P1 to P10 assigned to the upper brain layer G1, image P5 located at the center (centre) in the slice thickness direction is selected as the representative image D. For example, when white matter high signal intensity areas W are not extracted only in the lower brain layer G3, image P25, for example, is selected from the lower brain layer G3, and images P4 and P16, for example, are selected from the upper brain layer G1 and middle brain layer G2, following the procedure described above.

[0043] (Image provision process S5) In the image providing step S5, the image providing unit 45 presents the three representative images D selected by the image selecting unit 44 (images P4, P16, and P23). The image providing unit 45 arranges the three representative images D, for example, in the order of the groups, in the vertical or horizontal direction. When arranging them in the vertical direction, for example, image P4 is arranged on the top, image P16 in the center, and image P23 on the bottom. When arranging them in the horizontal direction, for example, image P4 is arranged on the right, image P16 in the center, and image P23 on the left. The image providing unit 45 generates image information (presentation image Q) in which the representative images D are arranged, and further generates a medical checkup report R including the presentation image Q. The presentation image Q is not limited to image information in which three representative images D are arranged, but may be any image format (image data) in which the three representative images D are displayed simultaneously or at different times.

[0044] (Image display step S6) The image providing unit 45 outputs (provides) the presented image Q and the medical examination report R to the outside via a network such as a LAN or the Internet. The PACS server 20 receives the presentation image Q from the image provider 45. As a result, the client PC 15 connected to the PACS server 20 can download the presentation image Q and the medical checkup report R and print or display them. This eliminates the need for the doctor (radiography interpreter) operating client PC 15 to visually check all of the cross-sectional images (images P), and allows the doctor to confirm the extent and volume of the white matter high signal intensity areas W at a glance at presented image Q. In addition, presented image Q and medical checkup report R can be presented to the patient (subject) undergoing a brain checkup.

[0045] The mobile information terminal 50 owned by the person undergoing the brain checkup (subject) also receives the medical checkup report R including the presented image Q from the image providing unit 45. The examinee operates the mobile information terminal 50 to connect to the brain image providing device 30 via the Internet. Then, the medical checkup report R is downloaded from the image providing unit 45. This allows the examinee (subject) of the brain checkup to view the medical checkup report R.

[0046] In this way, the brain image providing system 1, brain image providing device 30, and brain image providing method according to the embodiments of the present invention can provide and present useful and highly satisfying image information (presented image Q, medical checkup report R) to subjects of brain checkups. In other words, the extent and volume of white matter high signal intensity areas W are visualized, allowing subjects to easily check their own symptoms, etc. It also makes it possible to closely examine cerebrovascular disorders that may be due to aging, lifestyle habits, etc.

[0047] Although the preferred embodiments of the present invention have been described above, the present invention is not limited to these embodiments, and various modifications and changes are possible within the scope of the gist of the present invention.

[0048] FIG. 8 shows white matter hyperintensity areas W (hyperintensity areas around the lateral ventricles X, and hyperintensity areas deep subcortical Y). In the embodiment described above, in the image selection step S4, the specific region extraction unit 41 extracted white matter high signal intensity regions W from multiple images P, and the shape measurement unit 42 measured their areas. Furthermore, the white matter high signal intensity regions W may be subdivided (classified) and similar processing may be performed. That is, the white matter high signal intensity regions W are subdivided into peri-lateral ventricle high signal intensity regions X and deep subcortical high signal intensity regions Y. The specific region extraction unit 41 extracts a high signal intensity area X around the lateral ventricle from each image P, and the shape measurement unit 42 measures its area. Similarly, the specific region extraction unit 41 extracts a deep subcortical high signal intensity area Y from each image P, and the shape measurement unit 42 measures its area. Then, the image selection unit 44 selects, as the representative image D, the image in which the area of ​​the high signal intensity region X around the lateral ventricle is the largest or the image in which the area of ​​the deep subcortical high signal intensity region Y is the largest.

[0049] Furthermore, the white matter high signal intensity area W may be further divided into the following four areas for extraction and classification (grouping): subcortical, deep area, periventricular, and corpus callosum.

[0050] Instead of G1 to G3, the image partitioning unit 43 may set (partition) groups based on the presence or absence of white matter hyperintensity areas W. Specifically, a group G4 containing white matter hyperintensity areas W and a group G5 containing no white matter hyperintensity areas W may be set. Groups may also be set according to the presence or absence of a lateral ventricular periphery high signal area X or a deep subcortical high signal area Y. Specifically, group G6 is set in which a lateral ventricular periphery high signal area X is present, group G7 is set in which a deep subcortical high signal area Y is present, and group G8 is set in which neither a lateral ventricular periphery high signal area X nor a deep subcortical high signal area Y is present. In this group setting, the same image P may be assigned to multiple groups in a duplicated manner (duplication is permitted). For example, if image P4 contains a periventricular high signal area X and a deep subcortical high signal area Y, image P4 is assigned to both group G6 and group G7 in a duplicated manner.

[0051] In the above-described embodiment, a cross-sectional image of the brain is used, but cross-sectional images of other organs (internal organs such as the liver, teeth, and bones) may also be used. Although the specific region extraction unit 41 extracted the white matter high signal intensity region W from the image P as the specific region, it may extract other types of regions. The specific region extraction unit 41 may extract, for example, a stroke region. Furthermore, the stroke region may be subdivided (classified) into the following three types for extraction and classification. Specifically, the stroke region may be subdivided into cerebral hemorrhage and cerebral hematoma (high absorption region), cerebral infarction (low absorption region), and early CT sign (unclear boundary), for extraction and classification, etc.

[0052] In the above-described embodiment, the shape measuring unit 42 measures the area as the shape of a specific region (e.g., white matter hyperintensity region W), but other shapes may be measured. The shape measuring unit 42 may measure the major axis, minor axis, perimeter, etc. as the shape of a specific region. The image selection unit 44 may select, for example, as the representative image D, the image (image P) having the longest perimeter of the white matter high signal intensity region W.

[0053] The specific region extraction unit 41 may obtain a three-dimensional image of the specific region based on position information in the slice thickness direction of the image P and position information on the slice plane of the specific region. This enables the shape measurement unit 42 to measure the volume of this three-dimensional image. The image selection unit 44 may select the representative image D from the image (the plurality of images P) having the largest volume of the stereoscopic image of the specific region.

[0054] The image division unit 43 can set groups based on position information of specific regions (e.g., white matter high signal areas W) on the slice plane. For example, it distinguishes between specific regions existing in the right brain and specific regions existing in the left brain (creating different groups and dividing them), and it distinguishes between specific regions existing in the frontal lobe and specific regions existing in the occipital lobe.

[0055] The specific region extraction unit 41 obtains a three-dimensional image of the specific region based on position information in the slice thickness direction of the image and position information on the slice plane of the specific region. Then, the image division unit 43 sets groups based on the presence or absence of this three-dimensional image. The specific region is not a flat body but exists as a three-dimensional object (mass). Therefore, by finding and classifying (sorting) the three-dimensional object (three-dimensional image) of the specific region, it is possible to efficiently select a noteworthy image P as a representative image D. Specifically, when there are two stereoscopic images (images A and B) of white matter hyperintensity regions W, the system sets up group G9 containing image A, group G10 containing image B, and group G11 containing neither image A nor image B. This makes it possible to reliably select noteworthy images P from each of the two white matter hyperintensity region masses (groups) as representative images D.

[0056] The image partitioning unit 43 may set the groups based on position information in the slice thickness direction or slice plane of the stereoscopic image. For example, distinguish between the stereoscopic images of a specific area in the right brain and the stereoscopic images of a specific area in the left brain (by creating different groups and sorting them). Distinguish between the stereoscopic images of a specific area in the frontal lobe and the stereoscopic images of a specific area in the occipital lobe. For example, if there are multiple stereoscopic images of a specific region in the right brain, these may be grouped together, thereby drawing attention to a specific region in a specific location (such as the right brain).

[0057] A case where a plurality of representative images D are similar to each other will be described. Images P adjacent or close to each other in the slice thickness direction may be selected from different groups as representative images D. For example, image P10 of group G1 and image P11 of group G2 are selected as representative images D. Furthermore, when one image P is assigned to multiple groups in a duplicated manner, the same image P may be selected from different groups as the representative image D. For example, image P4 is assigned to both group G6 and group G7 in a duplicated manner, and image P4 is selected as the representative image D for group G6 and group G7. In these cases, the representative images D are similar to each other, and the usefulness of the presented image Q (medical examination report R) decreases.

[0058] Therefore, the representative image D is reselected as follows. The image selection unit 44 calculates the similarity between the representative images D. If the similarity exceeds a predetermined reference value, the representative image D is reselected. The similarity is calculated based on the position information in the slice thickness direction of the representative image D. For example, if the representative images D are located within 10 mm apart in the slice thickness direction, the representative image D is reselected. In other words, "10 mm" is set as the "predetermined reference value" of the similarity. The image selection unit 44 then changes the type of measurement value when reselecting the representative image D. For example, instead of selecting the area of ​​the specific region (white matter hyperintensity region W), the image selection unit 44 selects the image P (representative image D) in which the perimeter of the specific region is the largest.

[0059] In the above-described embodiment, the image division step S3 is performed after the specific region extraction step S1 and the shape measurement step S2, but the order is not limited to this. If the grouping is not based on the characteristics of the specific region, the image division step S3 may be performed before the specific region extraction step S1. In this case, the image division step S3 only involves setting a plurality of groups. On the other hand, when grouping is performed based on the characteristics of the specific region, the image division step S3 is performed after the specific region extraction step S1 and the shape measurement step S2.

[0060] In the above-described embodiment, among the cross-sectional images of the subject's brain, a horizontal cross-sectional image perpendicular to the body axis direction is used, but a different cross-sectional image may be used. A frontal cross-sectional image perpendicular to the sagittal horizontal axis direction or a sagittal cross-sectional image perpendicular to the frontal horizontal axis direction may be used. All of the horizontal cross-sectional image, frontal cross-sectional image, and sagittal cross-sectional image may be used, or any one or two of them may be used.

[0061] The brain image providing device 30 may be either a cloud type or an on-premise type. The connection (intercommunication) between the brain image providing device 30 and the PACS server 20 may be via either a LAN or the Internet. [Explanation of symbols]

[0062] 1. Brain image provision system (image provision system) 10 Brain imaging device (imaging device) 20 PACS Servers 15 Client PCs 30 Brain imaging device (image providing device) 31 Control Unit 311 CPU 41 Specific area extraction part 42 Shape measurement section 43 Image division section 44 Image Selection Section 45 Image provided by 50 Portable information terminal (terminal device) G1~G11 Group P, P1~P20 images (cross-sectional images) D. Representative image Q Presentation image R Medical Checkup Report W White matter hyperintensity area (specific area) X High signal area around the lateral ventricles (specific area) Y Deep subcortical high signal area (specific area)

Claims

1. a specific region extraction unit that extracts a specific region from a plurality of cross-sectional images of an organ of a subject; A shape measuring unit for measuring the shape of the specific region; an image division unit that divides each of the cross-sectional images into a plurality of groups; an image selection unit that selects a representative image from each of the groups based on the measurements by the shape measurement unit; an image providing unit that provides the representative image; An image providing device comprising:

2. the organ is the brain, The image providing device according to claim 1 , wherein the specific region is a white matter hyperintensity region.

3. the organ is the brain, The image providing device according to claim 1 , wherein the specific region is a stroke region.

4. The image providing device according to claim 1 , wherein the specific region extraction section extracts the specific region by pattern recognition.

5. The image providing device according to claim 1 , wherein the shape measuring section measures an area, a major axis, a minor axis, or a perimeter of the specific region.

6. the specific region extraction unit obtains a three-dimensional image of the specific region based on position information of the cross-sectional image in a slice thickness direction and position information of the specific region on a slice plane; The image providing device according to claim 1 , wherein the shape measuring section measures a volume of the three-dimensional image.

7. The image providing device according to claim 1 , wherein the image partitioning section sets the groups based on position information of the cross-sectional images in a slice thickness direction.

8. The image providing device according to claim 1 , wherein the image dividing section sets the groups based on the presence or absence of the specific area.

9. The image providing device according to claim 8 , wherein the image partitioning section sets the groups based on position information on a slice plane of the specific region.

10. The image providing device according to claim 8 , wherein the image dividing section divides the specific area into a plurality of categories, and sets the groups based on the categories.

11. the specific region extraction unit obtains a three-dimensional image of the specific region based on position information of the cross-sectional image in a slice thickness direction and position information of the specific region on a slice plane; The image providing device according to claim 1 , wherein the image dividing section sets the groups based on the presence or absence of the stereoscopic image.

12. The image providing device according to claim 11 , wherein the image dividing section sets the groups based on position information in a slice thickness direction or on a slice plane of the three-dimensional image.

13. 2. The image providing device according to claim 1, wherein the image selection section determines a similarity between the representative images, and when the similarity exceeds a predetermined reference value, reselects the representative image.

14. The image providing device according to claim 13 , wherein the similarity is calculated based on position information of the representative image in a slice thickness direction.

15. The image providing device according to claim 13 , wherein the image selection section changes a type of the measurement value and reselects the representative image.

16. When the specific region is not extracted from the cross-sectional image in the group, the image selection unit The image providing device according to claim 1 , wherein the representative image is selected based on position information of the cross-sectional image in a slice thickness direction.

17. An image providing device according to any one of claims 1 to 16; an image capturing device for capturing the cross-sectional image; a PACS server that receives the cross-sectional images from the image capturing device and transfers them to the image providing device; Equipped with An image providing system, wherein the image providing device transmits the representative image to the PACS server.

18. a terminal device that communicates with the image providing device via an electric communication network by operation of the subject, The image providing system according to claim 17 , wherein the image providing device transmits the representative image to the terminal device based on an instruction from the terminal device.

19. A specific region extraction step of extracting a specific region from a plurality of cross-sectional images of an organ of a subject; a shape measuring step of measuring a shape of the specific region; an image dividing step of dividing each of the cross-sectional images into a plurality of groups; an image selection step of selecting a representative image from each of the groups based on the measurements obtained in the shape measurement step; an image providing step of providing the representative image; The image providing method according to the present invention comprises:

20. A CPU of the image providing device, a specific region extraction unit that extracts a specific region from a plurality of cross-sectional images of an organ of a subject; A shape measuring unit for measuring the shape of the specific region; an image division unit that divides each of the cross-sectional images into a plurality of groups; an image selection unit that selects a representative image from each of the groups based on the measurements by the shape measurement unit; an image providing unit that provides the representative image; A program to function as a

Images