JP6882216B2 - Diagnostic support system, diagnostic support method, and program - Google Patents

Description

The present disclosure relates to diagnostic support systems, diagnostic support methods, and programs.

Conventionally, in order to realize diagnostic support for multiple organs and multiple diseases, a common platform having a mechanism for sharing and using a diagnostic support algorithm has been proposed (see Non-Patent Document 1).

Yukitaka Nimura, Daisuke Deguchi, Takayuki Kitasaka, Kensaku Mori, Yasuhito Suenaga, "PLUTO: Common Platform for Medical Imaging Diagnosis Support", MEDICAL IMAGING TECHNOLOGY Vol.26 No.3, P.187-191, May 2008

However, although Non-Patent Document 1 describes that it provides a common platform for realizing diagnostic support for multiple organs and multiple diseases, it is necessary to evaluate diseases by combining a plurality of organ information. Is not described.

The present disclosure has been made in view of the above circumstances, and an object of the present disclosure is to provide a diagnostic support system, a diagnostic support method, and a program capable of evaluating a disease from a combination of a plurality of organ information. ..

In order to achieve the above object, the diagnostic support system of the present disclosure includes an acquisition unit that acquires a medical image, an extraction unit that extracts an organ region in which an organ is drawn from the medical image acquired by the acquisition unit, and an extraction unit. The first derivation unit that derives the organ information of the organ region extracted by, and the first organ information derived for the first organ region extracted from the first medical image acquired by the acquisition unit, and acquisition. Based on the second organ information derived for the second organ region extracted from the second medical image acquired by the department, and the second derivation unit for deriving the index value related to the evaluation of the disease based on the second organ information. Including.

In the diagnostic support system of the present disclosure, the second medical image is the same image as the first medical image, and a plurality of organ regions may be included in the image.

Further, in the diagnostic support system of the present disclosure, the first derivation unit may derive the first organ information from the organ region extracted from at least the first medical image.

Further, in the diagnostic support system of the present disclosure, after the extraction unit extracts the organ region of the entire organ existing in the medical image, a part of the extracted organ region is further extracted, and the first derivation unit uses the first derivation unit. Partial organ information is derived from a part of the region, and the second derivation part is the first part of the organ information derived for a part of the first organ region extracted from the first medical image. , The index value may be derived based on the second partial organ information derived for a partial region of the second organ region extracted from the second medical image.

Further, in the diagnostic support system of the present disclosure, the first organ region and the second organ region may be the same organ region.

Further, in the diagnostic support system of the present disclosure, the extraction unit is a medical image including a first medical image and an organ existing in the first medical image, and a medical image other than the first medical image. The organ region may be extracted from and.

Further, in the diagnostic support system of the present disclosure, the first medical image and the medical image other than the first medical image may be different images in the same examination.

Further, in the diagnostic support system of the present disclosure, a medical image other than the first medical image may be a medical image obtained by taking a medical image earlier than the first medical image.

Further, in the diagnostic support system of the present disclosure, the first medical image and the medical image other than the first medical image may be a medical image obtained by photographing by a different imaging method.

Further, in the diagnosis support system of the present disclosure, even if the organ information includes at least one of the size of the organ region, the signal value of each pixel in the image of the organ region, and the signal value of each pixel. Good.

Further, in the diagnostic support system of the present disclosure, the index value may include at least one of information representing the degree of disease progression and the area to be evaluated for the disease.

Further, in the diagnostic support system of the present disclosure, the first organ region is the spleen region, the first organ information is the average value of the signal values of the pixels in the spleen region, and the second organ region is. , The second organ information is the signal value of each pixel in the liver region, the disease is fatty liver, and the index value is the signal value of the pixel in the liver region of the spleen. The information may represent an area smaller than a predetermined value or more than the average value in the area.

Further, in the diagnostic support system of the present disclosure, the first organ region is the lung region, the first organ information is the size of the tumor and the presence or absence of invasion in the lung region, and the second organ region. Is the area of the lymph nodes and the area of the lungs and other organs other than the lymph nodes, and the second organ information is the presence or absence of lymph node metastasis and the presence or absence of tumors in the area of other organs. , Lung cancer, and the index value may be the stage of lung cancer.

Further, in the diagnostic support system of the present disclosure, the first organ region is the spinal region, the first organ information is the third lumbar vertebra to the fourth lumbar vertebra, and the second organ region is the psoas major muscle. The second organ information is the cross-sectional area of each of the plurality of cross sections of the psoas major muscle region, the disease is sarcopenia, and the index value is the psoas major muscle in the third to fourth lumbar vertebrae. It may be the cross-sectional area of the region of.

Further, the diagnostic support system of the present disclosure includes a display control unit that controls the display of an image of the organ region extracted by the extraction unit on the display unit, and a reception unit that accepts user corrections to the organ region displayed on the display unit. And, when the first derivation unit receives the correction by the reception unit, the organ information of the organ region after the correction may be derived.

Further, the diagnostic support system of the present disclosure may further include a display control unit that controls the display of the index value derived by the second derivation unit on the display unit.

Further, in the diagnostic support system of the present disclosure, the display control unit further displays at least one of the first organ region, the second organ region, the first organ information, and the second organ information on the display unit. Control may be performed.

Further, the diagnosis support system of the present disclosure may further include a display control unit that controls the display of the diagnosis result using the index value derived by the second derivation unit on the display unit.

In addition, the diagnostic support system of the present disclosure corresponds to each organ region corresponding to each of the plurality of types of index values when index values related to the evaluation of a plurality of types of diseases are derived by the second derivation unit. It may further include an allocation unit that assigns an observer.

On the other hand, in order to achieve the above object, the diagnostic support method of the present disclosure acquires a medical image, extracts an organ region in which an organ is depicted from the acquired medical image, and derives organ information of the extracted organ region. , The first organ information derived for the first organ region extracted from the acquired first medical image, and the second organ information derived for the second organ region extracted from the acquired second medical image. , The diagnostic support system executes the process of deriving the index value related to the evaluation of the disease.

Further, in order to achieve the above object, the program of the present disclosure acquires a medical image, extracts an organ region in which an organ is depicted from the acquired medical image, derives organ information of the extracted organ region, and acquires the organ information. The first organ information derived from the first medical image extracted from the first medical image and the second organ information derived from the second organ region extracted from the acquired second medical image. Based on this, the purpose is to cause a computer to perform a process of deriving an index value related to the evaluation of a disease.

According to the present disclosure, a disease can be evaluated from a combination of a plurality of organ information.

It is a block diagram which shows an example of the structure of the diagnosis support system which concerns on embodiment. It is a block diagram which shows an example of the hardware configuration of the diagnosis support apparatus which concerns on embodiment. It is a figure which shows an example of the allocation table which concerns on embodiment. It is a block diagram which shows an example of the functional structure of the diagnostic support apparatus which concerns on embodiment. It is a figure which shows an example of the organ region extracted from the medical image which concerns on embodiment. It is a flowchart which shows an example of the diagnosis support processing which concerns on embodiment. It is a figure which shows an example of the region of the spine extracted from the medical image which concerns on embodiment. It is a figure which shows an example of the region of the psoas major muscle extracted from the medical image which concerns on embodiment. It is a figure which shows an example of the index value display screen which concerns on embodiment.

Hereinafter, examples of embodiments for carrying out the technique of the present disclosure will be described in detail with reference to the drawings.

First, the configuration of the diagnostic support system 10 according to the present embodiment will be described with reference to FIG. As shown in FIG. 1, the diagnostic support system 10 includes an image management device 12 and a diagnostic support device 14. The image management device 12 and the diagnosis support device 14 are each connected to the network N, and communication via the network N is possible. The image management device 12 stores medical image data indicating a medical image obtained by imaging with an imaging device that captures a medical image such as CT (Computed Tomography) and MRI (Magnetic Resonance Imaging). An example of the image management device 12 is a PACS (Picture Archiving and Communication System). The diagnosis support device 14 supports diagnosis by using the medical image data stored in the image management device 12. Examples of the diagnostic support device 14 include information processing devices such as personal computers and server computers.

Next, the hardware configuration of the diagnostic support device 14 according to the present embodiment will be described with reference to FIG. As shown in FIG. 2, the diagnostic support device 14 includes a CPU (Central Processing Unit) 20, a memory 21 as a temporary storage area, and a non-volatile storage unit 22. Further, the diagnostic support device 14 includes a display unit 23 such as a liquid crystal display, an input unit 24 such as a keyboard and a mouse, and a network I / F (InterFace) 25 connected to the network N. The CPU 20, the memory 21, the storage unit 22, the display unit 23, the input unit 24, and the network I / F 25 are connected to the bus 26.

The storage unit 22 is realized by an HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory, or the like. The diagnosis support program 30 is stored in the storage unit 22 as a storage medium. The CPU 20 reads the diagnostic support program 30 from the storage unit 22, expands it into the memory 21, and executes the expanded diagnostic support program 30.

Further, the allocation table 32 is stored in the storage unit 22. FIG. 3 shows an example of the allocation table 32. As shown in FIG. 3, the allocation table 32 according to the present embodiment is information in which each of a plurality of types of organs is associated with a doctor in charge of the organs. A doctor is an example of an observer.

Next, with reference to FIG. 4, the functional configuration of the diagnostic support device 14 according to the present embodiment will be described. As shown in FIG. 4, the diagnosis support device 14 includes an acquisition unit 40, an extraction unit 42, a reception unit 44, a first out-licensing unit 46, a second out-licensing unit 48, a display control unit 50, and an allocation unit 52. When the CPU 20 executes the diagnosis support program 30, it functions as an acquisition unit 40, an extraction unit 42, a reception unit 44, a first derivation unit 46, a second derivation unit 48, a display control unit 50, and an allocation unit 52.

The acquisition unit 40 acquires the medical image data stored in the image management device 12 from the image management device 12 via the network N. In the present embodiment, the acquisition unit 40 acquires a plurality of medical image data indicating each of a plurality of medical images in the same examination, such as a plurality of medical images obtained by one CT imaging.

The extraction unit 42 extracts an organ region in which an organ is drawn from a plurality of medical images indicated by a plurality of medical image data acquired by the acquisition unit 40. The extraction unit 42 may extract a plurality of organ regions from each of a plurality of different medical images.

For example, AI (Artificial Intelligence) technology can be applied to the extraction process of the organ region by the extraction unit 42. Specifically, for example, a deep neural network is created by inputting medical image data and outputting the extracted organ region. Next, the trained model obtained by training the created deep neural network using the medical image data and the teacher data including the organ region in the medical image indicated by the medical image data is stored in the storage unit 22 in advance. I will do it. The extraction unit 42 inputs a plurality of medical image data acquired by the acquisition unit 40 into the trained model, acquires the organ region output from the trained model, and extracts the organ region from the medical image. FIG. 5 shows an example of the organ region extracted by the extraction unit 42. The extraction unit 42 may extract a region input by the user via the input unit 24 as an organ region.

The reception unit 44 receives the modification by the user for the organ region displayed on the display unit 23 via the input unit 24.

The first derivation unit 46 derives organ information obtained from an image of each organ region for a plurality of organ regions extracted by the extraction unit 42. Examples of organ information include the size of the organ region such as area and volume, and the signal value (for example, pixel value) of each pixel of the image of the organ region. Further, examples of the organ information include values calculated from the signal values of each pixel of the image of the organ region (for example, maximum value, minimum value, average value, variance, standard deviation, etc.). Further, when the reception unit 44 receives the correction by the user, the first derivation unit 46 derives the organ information of the organ region after the correction by the user.

For example, AI technology can be applied to the process of deriving the organ information by the first deriving unit 46. Specifically, for example, a deep neural network is created by inputting image data showing an image of an organ region and outputting organ information. Next, a trained model obtained by training the created deep neural network using image data showing an image of an organ region and teacher data including organ information corresponding to the image data is stored in the storage unit 22 in advance. Remember. The first derivation unit 46 is extracted by the extraction unit 42 by inputting image data showing an image of the organ region extracted by the extraction unit 42 into the trained model and acquiring the organ information output from the trained model. The organ information of the specified organ region is derived.

The second derivation unit 48 derives an index value related to the evaluation of the disease based on the plurality of organ information derived for each of the plurality of organ regions by the first derivation unit 46. The number of organ information may be two or three or more.

For example, AI technology can be applied to the index value derivation process by the second derivation unit 48. Specifically, for example, a deep neural network is created in which information on a plurality of organs is input and index values related to disease evaluation are output. Next, the storage unit 22 stores the trained model obtained by training the created deep neural network in advance using the teacher data including the plurality of organ information and the index value related to the evaluation of the disease corresponding to the organ information. Remember. The second derivation unit 48 inputs the organ information derived by the first derivation unit 46 for a plurality of organ regions into the trained model, and acquires the index value related to the evaluation of the disease output from the trained model. Derivation of index values for disease evaluation. The second derivation unit 48 may derive an index value related to the evaluation of the disease according to a predetermined rule instead of using the AI technique.

The display control unit 50 controls the display unit 23 to display an image of the organ region extracted by the extraction unit 42. For example, the display control unit 50 performs image processing to fill the medical image to be extracted of the organ region with the organ region extracted by the extraction unit 42 with a predetermined color for each organ region. Is controlled to be displayed on the display unit 23. Further, the display control unit 50 controls to display the index value related to the evaluation of the disease derived by the second out-licensing unit 48 on the display unit 23.

When the index value related to the evaluation of a plurality of types of diseases is derived by the second derivation unit 48, the allocation unit 52 allocates each organ region corresponding to each of the plurality of types of index values to an observer observing each organ region. .. Specifically, the allocation unit 52 refers to the allocation table 32 and assigns a specialist who specializes in the organ region to the organ region corresponding to the index value as an observer.

Next, the operation of the diagnostic support device 14 according to the present embodiment will be described with reference to FIG. When the CPU 20 executes the diagnosis support program 30, the diagnosis support process shown in FIG. 6 is executed. Further, the diagnostic support process shown in FIG. 6 is executed when, for example, the user inputs an execution instruction of the diagnostic support program 30 via the input unit 24.

In step S10 of FIG. 6, as described above, the acquisition unit 40 acquires the medical image data stored in the image management device 12 from the image management device 12 via the network N. In step S12, the extraction unit 42 extracts a plurality of organ regions in which organs are drawn from a plurality of medical images indicated by the plurality of medical image data acquired by the process of step S10.

In step S14, as described above, the display control unit 50 controls the display unit 23 to display the image of the organ region extracted by the process of step S12. The user visually confirms the organ region displayed on the display unit 23, and if it is not extracted correctly, corrects the organ region via the input unit 24. In step S16, the reception unit 44 determines whether or not the user's modification to the organ region has been accepted by the process of step S14. If this determination is a negative determination, the process proceeds to step S20, and if this determination is affirmative, the process proceeds to step S18. In step S18, the reception unit 44 modifies the organ region extracted by the process of step S12 according to the modification received by the process of step S16.

In step S20, as described above, the first derivation unit 46 derives organ information obtained from the images of the respective organ regions for the plurality of organ regions extracted by the process of step S12. When the organ region is modified by the process of step S18, the first derivation unit 46 derives the organ information obtained from the image of the modified organ region. In step S22, as described above, the second derivation unit 48 derives the index value related to the evaluation of the disease based on the plurality of organ information derived for each of the plurality of organ regions by the process of step S20.

In step S24, the allocation unit 52 determines whether or not the index value related to the evaluation of a plurality of types of diseases has been derived by the process of step S22. If this determination is a negative determination, the process proceeds to step S28, and if this determination is affirmative, the process proceeds to step S26. In step S26, as described above, the allocation unit 52 refers to the allocation table 32 and observes each organ region in each organ region corresponding to each of the plurality of types of index values derived by the process of step S22. Assign a person.

In step S28, the display control unit 50 controls the display unit 23 to display the index value related to the evaluation of the disease derived by the process of step S22. When the process of step S28 is completed, the diagnosis support process is completed.

Next, a specific operation example of the diagnosis support device 14 will be described.

<Operation example 1>
First, an operation example in which the diagnostic support device 14 derives an index value related to the evaluation of fatty liver as an index value related to the evaluation of a disease will be described.

The extraction unit 42 extracts the spleen region and the liver region from the plurality of medical images (step S12). The first derivation unit 46 derives an average value of pixel values (for example, CT values) in the spleen region as organ information in the spleen region, and pixels of each pixel in the liver region as organ information in the liver region. A value is derived (step S20).

In the second derivation unit 48, as an index value related to the evaluation of fatty liver, the pixel value of each pixel in the liver region is a predetermined value (for example, 10HU (Hounsfield Unit)) than the average value of the pixel values in the spleen region. Information representing a region that is smaller than this and whose pixel value is equal to or less than a threshold value (for example, 40 HU) is derived (step S22). The information representing this area is an example of the information representing the area to be evaluated for the disease.

The display control unit 50 controls to visually display the information representing the region derived by the second out-licensing unit 48 on the display unit 23 (step S28). The doctor evaluates fatty liver based on the information displayed on the display unit 23.

<Operation example 2>
Next, an operation example in which the diagnosis support device 14 derives an index value related to the evaluation of lung cancer as an index value related to the evaluation of the disease will be described.

The extraction unit 42 extracts the lung region, the lymph node region, and the region of other organs other than the lung and the lymph node from the plurality of medical images (step S12). The first derivation unit 46 derives the size of the tumor and the presence or absence of infiltration in the lung region as organ information in the lung region. In addition, the first derivation unit 46 derives the presence or absence of lymph node metastasis as organ information in the lymph node region. In addition, the first derivation unit 46 derives the presence or absence of a tumor (that is, distant metastasis) in another organ as organ information in the region of another organ (step S20).

The second out-licensing unit 48 is an index for evaluating lung cancer based on the size and presence / absence of tumor size and infiltration in the lung region derived by the first out-licensing unit 46, the presence / absence of lymph node metastasis, and the presence / absence of distant metastasis. As a value, a stage as an example of the degree of lung cancer progression is derived (step S22). As a method used for deriving the stage of lung cancer, for example, the method disclosed in Reference 1 below can be applied.
[Reference 1] "Classification of stage", [online], [Search on February 6, 2018], Internet (URL: https://ganclass.jp/kind/lung/stage/stage.php)

The display control unit 50 controls to display the stage of lung cancer derived by the second out-licensing unit 48 on the display unit 23 (step S28). The doctor evaluates lung cancer based on the information displayed on the display unit 23.

<Operation example 3>
Next, an operation example in which the diagnostic support device 14 derives an index value related to the evaluation of sarcopenia as an index value related to the evaluation of the disease will be described.

The extraction unit 42 extracts a region of the spine and a region of the psoas major muscle from a plurality of medical images (step S12). An example of the spinal region extracted by the extraction unit 42 is shown in FIG. 7, and an example of the psoas major muscle region is shown in FIG. The first derivation unit 46 derives the third lumbar vertebra to the fourth lumbar vertebra as the organ information of the spinal region, and derives the cross-sectional area of each of the plurality of cross sections of the psoas major muscle as the organ information of the psoas major muscle region. (Step S20).

The second derivation unit 48 uses the cross-sectional area of the psoas major muscle in the 3rd to 4th lumbar vertebrae as an index value for the evaluation of sarcopenia (step S22). The display control unit 50 controls the display unit 23 to display the cross-sectional area of the psoas major muscle in the third lumbar vertebra to the fourth lumbar vertebra derived by the second derivation unit 48 (step S28). FIG. 9 shows an example of an index value display screen for displaying the index value related to the evaluation of sarcopenia. The doctor evaluates sarcopenia based on the information displayed on the display unit 23. Specifically, the change over time in the cross-sectional area of the psoas major muscle in the 3rd to 4th lumbar vertebrae is used as an index value of the degree of progression of sarcopenia.

As described above, according to the present embodiment, it is possible to evaluate a disease including the presence or absence of a disease from a combination of a plurality of organ information.

In the above embodiment, the case where a plurality of organ regions are extracted from a plurality of medical images has been described, but the present invention is not limited to this. For example, a plurality of organ regions may be extracted from one medical image.

Further, in the above embodiment, the case of extracting the region of the entire organ such as the liver and the spleen has been described, but the present invention is not limited to this. After the extraction unit 42 extracts the region of the entire organ, a part of the region may be further extracted from the extracted region. In this case, the first derivation unit 46 exemplifies a form in which a part of organ information is derived from an image of a part of a region extracted by the extraction unit 42. Further, in this case, the second out-licensing unit 48 may derive an index value related to disease evaluation based on a plurality of partial organ information derived from each of the plurality of partial regions by the first out-licensing unit 46. Illustrated. Further, in this case, the plurality of partial regions may be partial regions extracted from the same organ region, such as the upper left lobe of the lung and the upper right lobe of the lung, or may be extracted from different organ regions. It may be a part of the area. Further, in this case, the plurality of organ regions used for deriving the index value related to the evaluation of the disease may be a combination of the region of the whole organ and the region of a part of the organ.

Further, in the above embodiment, the case where the organ region is extracted from a plurality of medical images in the same examination has been described, but the present invention is not limited to this. For example, an organ region may be extracted from a plurality of medical images in which the same subject is photographed at different times. Further, for example, an organ region may be extracted from a plurality of medical images obtained by photographing the same subject by different imaging methods. Examples of different imaging methods in this case include imaging with different imaging devices such as CT imaging and MRI imaging. In addition, different imaging methods in this case include, for example, imaging with the same imaging device, but with and without a contrast medium, and normal imaging with mammography and tomosynthesis imaging. Can be mentioned.

In the above embodiment, the case where the index value related to the evaluation of the disease is displayed on the display unit 23 has been described, but the present invention is not limited to this. For example, in addition to the index value related to the evaluation of the disease, each organ information used for deriving the index value and at least one of each organ region corresponding to each organ information may be further displayed on the display unit 23.

Further, for example, the diagnosis result using the index value related to the evaluation of the disease may be displayed on the display unit 23. In this case, when the pixel value of each pixel in the liver region is smaller than the average value of the pixel values in the spleen region by a predetermined value or more and the area of the region where the pixel value is equal to or less than the threshold value is the predetermined value or more, the patient. An example is a mode in which information indicating that is a fatty liver is displayed on the display unit 23.

Further, in the above embodiment, the case where an observer who observes the organ is assigned corresponding to each of the plurality of types of organs has been described, but the present invention is not limited to this. For example, an observer who observes the disease may be assigned corresponding to each of the plurality of types of diseases. Further, for example, an observer who observes the disease at the degree of progression may be assigned according to each degree of progression of the disease.

Further, various processors other than the CPU may execute various processes executed by the CPU executing software (program) in the above embodiment. In this case, the processor is a PLD (Programmable Logic Device) whose circuit configuration can be changed after manufacturing an FPGA (Field-Programmable Gate Array) or the like, and an ASIC (Application Specific Integrated Circuit) or the like in order to execute a specific process. An example is a dedicated electric circuit or the like, which is a processor having a circuit configuration designed exclusively for the purpose. Further, the above-mentioned various processes may be executed by one of these various processors, or a combination of two or more processors of the same type or different types (for example, a plurality of FPGAs and a combination of a CPU and an FPGA). Etc.). Further, the hardware structure of these various processors is, more specifically, an electric circuit in which circuit elements such as semiconductor elements are combined.

Further, in the above embodiment, the mode in which the diagnosis support program 30 is stored (installed) in the storage unit 22 in advance has been described, but the present invention is not limited to this. The diagnostic support program 30 is provided in a form recorded on a recording medium such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), and a USB (Universal Serial Bus) memory. May be good. Further, the diagnosis support program 30 may be downloaded from an external device via a network.

10 Diagnosis support system 12 Image management device 14 Diagnosis support device 20 CPU
21 Memory 22 Storage unit 23 Display unit 24 Input unit 25 Network I / F
26 Bus 30 Diagnosis support program 32 Allocation table 40 Acquisition unit 42 Extraction unit 44 Reception unit 46 1st out-licensing unit 48 2nd out-licensing unit 50 Display control unit 52 Allocation unit N network

Claims (20)

The acquisition department that acquires medical images and
An extraction unit that extracts a plurality of different organ regions in which an organ is drawn from a medical image acquired by the acquisition unit, and an extraction unit.
A first derivation unit that derives organ information for each of a plurality of organ regions extracted by the extraction unit, and
The first organ information derived for the first organ region extracted from the first medical image acquired by the acquisition unit and the first organ information extracted from the second medical image acquired by the acquisition unit . A second derivation unit that derives an index value related to disease evaluation based on a combination of a second organ information derived for a second organ region different from the first organ region, and a second derivation unit.
Diagnostic support system including. The diagnostic support system according to claim 1, wherein the second medical image is the same image as the first medical image, and includes a plurality of organ regions in the image. The diagnostic support system according to claim 1 or 2, wherein the first derivation unit derives the first organ information from at least an organ region extracted from the first medical image. The extraction unit extracts the organ region of the entire organ existing in the medical image, and then further extracts a part of the extracted organ region.
The first derivation unit derives some organ information from the part of the region,
The second derivation unit was extracted from the first partial organ information derived for a part of the first organ region extracted from the first medical image and the second medical image. The diagnostic support according to any one of claims 1 to 3, which derives the index value based on the second partial organ information derived for a partial region of the second organ region. system. The extraction unit is a medical image including the first medical image and an organ existing in the first medical image, and the organ region is obtained from a medical image other than the first medical image. The diagnostic support system according to any one of claims 1 to 4 to be extracted. The diagnostic support system according to claim 5 , wherein the first medical image and medical images other than the first medical image are different images in the same examination. The diagnostic support system according to claim 5 , wherein the medical image other than the first medical image is a medical image obtained by taking a picture in the past than the first medical image. The diagnostic support system according to claim 5 , wherein the first medical image and a medical image other than the first medical image are medical images obtained by photographing by different imaging methods. The organ information, the size of the organ region, said signal value of each pixel in the image of the organ region, and the claims 1 to 8 comprising at least one of the values calculated using the signal value of each pixel The diagnostic support system according to any one of the above. The diagnostic support system according to any one of claims 1 to 9 , wherein the index value includes at least one of information indicating the degree of disease progression and the area to be evaluated for the disease. The first organ region is the region of the spleen.
The first organ information is the average value of the signal values of the pixels in the region of the spleen.
The second organ region is the region of the liver and
The second organ information is a signal value of each pixel in the region of the liver.
The disease is fatty liver
The index value, the signal value of the pixels in the region of the liver, from claim 1 than the average value in the region of the spleen, which is information representing a small area or a predetermined value according to any one of claims 1 0 Diagnostic support system. The first organ region is the lung region and
The first organ information is the size of the tumor and the presence or absence of infiltration in the area of the lung.
The second organ region is a region of lymph nodes and regions of lungs and other organs other than lymph nodes.
The second organ information is the presence or absence of lymph node metastasis and the presence or absence of a tumor in the region of the other organ.
The disease is lung cancer
The index value is, the diagnosis support system according to claim 1 to any one of claims 1 0 is the stage of lung cancer. The first organ region is the region of the spine.
The first organ information is the third lumbar vertebra to the fourth lumbar vertebra.
The second organ region is the region of the psoas major muscle.
The second organ information is the cross-sectional area of each of the plurality of cross sections of the psoas major muscle region.
The disease is sarcopenia
The index value is, the diagnosis support system according to any one of claims 1 0 to claim 1 is a cross-sectional area of the region of the psoas major in the third lumbar fourth lumbar vertebra. A display control unit that controls the display of an image of an organ region extracted by the extraction unit on the display unit.
A reception unit that accepts user corrections to the organ area displayed on the display unit, and
Including
Said first derivation unit, when modified by the accepting unit is accepted, the diagnosis support system according to any one of claims 1 to 3 claim 1 for deriving the organ information organ region after correction. Diagnosis support system according to any one of claims 1 to 3 claim 1, further comprising a display control unit that performs control to display on the display unit an index value derived by said second derivation unit. The display control unit further controls to display at least one of the first organ region, the second organ region, the first organ information, and the second organ information on the display unit. diagnosis support system according to claim 1 5. Diagnosis support system according to claim 1 to any one of claims 1 to 3, the display further comprising a control unit for controlling display on the display section a diagnosis result using an index value derived by said second derivation unit .. When index values related to the evaluation of a plurality of types of diseases are derived by the second derivation unit, an allocation unit for assigning an observer corresponding to each organ region is further assigned to each organ region corresponding to each of the plurality of types of index values. The diagnostic support system according to any one of claims 1 to 17, including. Get a medical image,
From the acquired medical images, multiple different organ regions in which the organs were depicted were extracted, and
Derived organ information for each of the extracted multiple organ regions,
Derived from the first organ information derived for the first organ region extracted from the acquired first medical image and the second organ region different from the first organ region extracted from the acquired second medical image. A diagnostic support method in which a diagnostic support system executes a process of deriving an index value related to disease evaluation based on a combination of the second organ information. Get a medical image,
From the acquired medical images, multiple different organ regions in which the organs were depicted were extracted, and
Derived organ information for each of the extracted multiple organ regions,
Derived from the first organ information derived for the first organ region extracted from the acquired first medical image and the second organ region different from the first organ region extracted from the acquired second medical image. A program for causing a computer to perform a process of deriving an index value related to disease evaluation based on a combination of the second organ information obtained.

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