JP7086759B2 - Diagnostic support device, diagnostic support method, and diagnostic support program - Google Patents

Description

Embodiments of the present invention relate to a diagnostic support device, a diagnostic support method, and a diagnostic support program.

In MR (Magnetic Resonance) inspection, it is often the case that images are taken under multiple imaging conditions at one time. For example, in the head MR examination, a plurality of MR images such as a DWI (Diffusion Weight Image), an ADC (Apparent Diffusion Coefficient) map, and a T2-weighted image are taken.

The image interpreting doctor interprets the acquired plurality of MR images while taking a plurality of procedures corresponding to each predetermined examination purpose. For example, in the interpretation related to the head MR examination, the interpretation doctor first diagnoses whether or not the patient has an acute cerebral infarction. Then, the presence or absence of other cerebral infarction and stenosis of blood vessels is diagnosed, and finally the presence or absence of structural abnormality (for example, inflammation) is diagnosed. In each procedure, the image interpreter makes a diagnosis by displaying, for example, any one of the acquired plurality of MR images and determining the correlation between the displayed plurality of MR images.

In general, it is necessary for the image interpreting doctor to select the MR image to be displayed and determine the correlation between the MR images. In recent years, with the development of medical image diagnostic devices such as MRI (Magnetic Resonance Imaging) devices and medical image management systems (PACS: Picture Archiving and Communication System), the amount of data that can be acquired by an image interpreter is increasing. For this reason, the burden on the interpreting doctor during interpretation is increasing.

International Publication No. 2013/06509

The problem to be solved by the invention is to reduce the burden of diagnosis in interpretation.

According to the embodiment, the diagnostic support device includes an image search unit, a feature amount extraction unit, and a correlation determination unit. The image search unit acquires an inspection purpose corresponding to the designated inspection, and searches for a medical image for an image type associated with the inspection purpose from a plurality of medical images acquired in the inspection. The feature amount extraction unit extracts the feature amount of the designated portion from the medical image obtained by the search. The correlation determination unit acquires a diagnostic result derived from the medical image of the image type based on the feature amount extracted from the medical image of the image type.

FIG. 1 is a block diagram showing a functional configuration of a system in a hospital provided with a diagnostic support device according to the first embodiment. FIG. 2 is a block diagram showing a functional configuration of the diagnostic support device shown in FIG. FIG. 3 is a diagram illustrating a process when an image interpreting doctor creates an image interpretation report using the diagnostic support device shown in FIG. FIG. 4 is a diagram showing a relationship table stored in the memory shown in FIG. FIG. 5 is a diagram showing a definition correlation table stored in the memory shown in FIG. FIG. 6 is a diagram illustrating the processing of the correlation determination function shown in FIG. FIG. 7 is a diagram illustrating a specific example of the processing of the correlation determination function shown in FIG. FIG. 8 is a diagram illustrating a specific example of the processing of the correlation determination function shown in FIG. FIG. 9 is a diagram illustrating a specific example of the processing of the correlation determination function shown in FIG. FIG. 10 is a diagram illustrating a specific example of the processing of the correlation determination function shown in FIG. FIG. 11 is a diagram illustrating a specific example of the processing of the correlation determination function shown in FIG. FIG. 12 is a diagram illustrating the processing of the finding creation function shown in FIG. FIG. 13 is a diagram illustrating a specific example of processing of the finding creation function shown in FIG. FIG. 14 is a block diagram showing a configuration of a diagnostic support system provided with the diagnostic support device according to the second embodiment. FIG. 15 is a block diagram showing a functional configuration of the diagnostic support device shown in FIG.

Hereinafter, embodiments will be described with reference to the drawings.

(First Embodiment)
FIG. 1 is a block diagram showing an example of a functional configuration of a system in a hospital provided with the diagnostic support device 10 according to the first embodiment. The systems shown in FIG. 1 include a diagnostic support device 10, a hospital information system (HIS) 20, a radiology information system (RIS) 30, and a medical image management system (PACS: Picture Archiving and Communication System). ) 40, a report system 50, a medical image diagnostic device 60, and a communication terminal 70. The diagnosis support device 10, the hospital information system 20, the radiation department information system 30, the medical image management system 40, the report system 50, the medical image diagnosis device 60, and the communication terminal 70 use an in-hospital network such as a LAN (Local Area Network). It is connected so that data communication is possible via. The connection to the hospital network may be a wired connection or a wireless connection. Further, as long as security is ensured, the connected line is not limited to the in-hospital network. For example, it may be connected to a public communication line such as the Internet via a VPN (Virtual Private Network) or the like.

The hospital information system 20 is a system that stores in-hospital information such as medical care information, patient information, and order information, and manages the stored in-hospital information. The medical information includes information related to electronic medical records, such as finding information, disease name information, vital information, examination stage information, and treatment content information. Patient information includes, for example, patient ID, patient name, gender, age, and the like. The order information is, for example, information that a medical doctor or the like requests a radiological examination, a sample examination, a physiological examination, a prescription, a medication, an image interpretation, or the like. When the order information is the inspection order information for requesting a radiological examination or the interpretation order information for requesting an image interpretation, the examination order information or the image interpretation order information is, for example, the examination date, the examination site, the examination purpose, and the medical diagnostic imaging apparatus. Includes information on diagnosis by type, requesting doctor, requesting department, etc.

The hospital information system 20 has, for example, a server device 21 and a communication terminal 22. The server device 21 and the communication terminal 22 are connected to each other via a network in the hospital so that data communication is possible. The server device 21 is an in-hospital information management server that stores medical information, patient information, order information, etc. in the hospital information system 20, and manages the stored medical information, patient information, order information, and the like.

Note that FIG. 1 shows an example in which the server included in the hospital information system 20 is only the server device 21, but the present invention is not limited to this. A plurality of server devices 21 may be provided as needed. For example, the server device 21 may be provided for each information to be managed. Specifically, for example, the hospital information system 20 has an electronic chart system that manages medical information, patient information, and the like, and an order entry system that issues order information to the medical department, and each system has an order information system. A server device may be provided in the system.

The communication terminal 22 is, for example, a terminal for a medical staff such as a medical doctor to input an instruction to the server device 21. Specifically, for example, the communication terminal 22 is operated by a medical doctor and outputs an instruction signal to the server device 21 so as to issue an inspection order to the radiation department information system 30. Upon receiving the instruction signal transmitted from the communication terminal 22, the server device 21 transmits the examination order information and the patient information to the radiation department information system 30. The examination order information and the patient information are transmitted to the radiation department information system 30 according to a preset standard, for example, HL7 (Health Level 7). Further, for example, the communication terminal 22 outputs an instruction signal to the server device 21 so as to issue an image interpretation order to the report system 50. Upon receiving the instruction signal transmitted from the communication terminal 22, the server device 21 transmits the interpretation order information and the patient information to the report system 50. The interpretation order information and the patient information are transmitted to the report system 50 according to a preset standard, for example, HL7.

The radiation department information system 30 is a system that manages information such as examination appointments in the radiation department, which is one of the medical treatment departments. The radiation department information system 30 has, for example, a server device 31 and a communication terminal 32. The server device 31 and the communication terminal 32 are connected so as to be capable of data communication via the in-hospital network.

The server device 31 is a department information management server that manages information such as inspection reservations in the radiation department. Specifically, for example, when the server device 31 receives the examination order information transmitted from the hospital information system 20 and the patient information, the server device 31 sets the examination order information in the radiology department. The examination order information in the radiology department includes detailed setting information required at the time of examination, for example, detailed information about the medical diagnostic imaging apparatus 60. Detailed examination order information set in the radiation department includes, for example, the presence or absence of a contrast agent, imaging conditions such as radiation dose when the medical image diagnostic device 60 is an X-ray CT (Computed Tomography) device, and an MRI device. If this is the case, imaging conditions and the like in various imaging protocols related to preparatory imaging and main imaging are included. The detailed examination order information may be preset based on the received examination order information, patient information, past examination history, and the like. The server device 31 transmits detailed examination order information and patient information to the medical diagnostic imaging device 60.

Note that FIG. 1 shows an example in which the server included in the radiation department information system 30 is only the server device 31, but the present invention is not limited to this. A plurality of server devices 31 may be provided as needed.

The communication terminal 32 is a terminal for a medical staff such as a radiologist to input an instruction to the server device 31 of the radiation department information system 30. Specifically, for example, the communication terminal 32 is operated by a radiologist, and based on the examination order information transmitted from the hospital information system 20 and the patient information, the server device 31 provides detailed examination order information in the radiology department. Enter in.

The medical image diagnosis device 60 is a device that generates medical image data by photographing a subject. The medical diagnostic imaging apparatus 60 includes, for example, an X-ray diagnostic apparatus, an X-ray CT apparatus, an MRI apparatus, an ultrasonic diagnostic apparatus, a SPECT (Single Photon Emission Computed Tomography) apparatus, a PET (Positron Emission computed Tomography) apparatus, a SPECT apparatus and an X. A SPECT-CT device in which a line CT device is integrated, a PET-CT device in which a PET device and an X-ray CT device are integrated, a PET-MRI device in which a PET device and an MRI device are integrated, or these. Equipment group, etc.

The medical image diagnosis device 60 photographs a patient based on the examination order information managed by the radiation department information system 30 and the patient information, and generates medical image data. The medical diagnostic imaging apparatus 60 converts the generated medical image data into an image file conforming to the DICOM (Digital Imaging and Communication Medicine) standard. The image file includes medical image data and incidental information added to the medical image data. The incidental information is information for managing medical image data, and includes, for example, an examination UID, a series UID, a patient ID, and the like. The inspection UID is an identifier that can uniquely identify the inspection. The series UID is, for example, an identifier that can uniquely identify a series of image groups acquired for each imaging site or for each imaging condition.

The medical image management system 40 is a system that stores medical image data and manages the stored medical image data. The medical image management system 40 has, for example, a server device 41. The server device 41 stores, for example, an image file created by the medical image diagnosis device 60 and converted according to the DICOM standard, and manages the stored image file. For example, the server device 31 transmits the requested image file among the stored image files to the image interpreting doctor in response to the request from the image interpreting doctor.

Note that FIG. 1 shows an example in which the server included in the medical image management system 40 is only the server device 41, but the present invention is not limited to this. A plurality of server devices 41 may be provided as needed.

The report system 50 is a system that manages the creation of an interpretation report by an interpretation doctor. The reporting system 50 has, for example, a server device 51.

The server device 51 manages various information for the image interpreter to create an image interpretation report. Specifically, for example, the server device 51 stores a management list that manages the unexecuted or already executed interpretation report for the interpretation order. When the server device 51 receives the interpretation order information and the patient information transmitted from the hospital information system 20, the server device 51 updates the management list. That is, when the server device 51 receives the image interpretation order information and the patient information, for example, one unread image interpretation order is added to the management list.

Note that FIG. 1 shows an example in which the server included in the report system 50 is only the server device 51, but the present invention is not limited to this. A plurality of server devices 51 may be provided as needed.

The communication terminal 70 is, for example, a terminal for a medical staff such as an image interpreting doctor to access a system and an apparatus connected to a LAN, for example, a viewer. Specifically, for example, the communication terminal 70 is operated by an image interpretation doctor and outputs a request signal requesting information for creating an image interpretation report to the diagnosis support device 10.

The diagnosis support device 10 is a device that supports the creation of an image interpretation report by supporting the diagnosis of a patient by an image interpretation doctor or the like. FIG. 2 is a block diagram showing an example of the functional configuration of the diagnostic support device 10 shown in FIG. The diagnostic support device 10 shown in FIG. 2 has a processing circuit 11, a memory 12, and a communication interface 13. The processing circuit 11, the memory 12, and the communication interface 13 are connected to each other so as to be able to communicate with each other via, for example, a bus.

The processing circuit 11 is a processor that functions as the center of the diagnostic support device 10. The processing circuit 11 realizes a function corresponding to the program by executing the program stored in the memory 12 or the like.

The memory 12 is a storage device such as a ROM (Read Only Memory), a RAM (Random Access Memory), an HDD (Hard Disk Drive), an SSD (Solid State Drive), and an integrated circuit storage device for storing various information. Further, the memory 12 may be a drive device or the like that reads / writes various information between a CD-ROM drive, a DVD drive, and a portable storage medium such as a flash memory. The memory 12 does not necessarily have to be realized by a single storage device. For example, the memory 12 may be realized by a plurality of storage devices. Further, the memory 12 may be in another computer connected to the diagnostic support device 10 via a network.

The memory 12 stores a diagnosis support program or the like according to the present embodiment. The diagnosis support program may be stored in the memory 12 in advance, for example. Further, for example, it may be stored and distributed in a non-transient storage medium, read from the non-transient storage medium, and installed in the memory 12.

The communication interface 13 performs data communication with the hospital information system 20, the radiation department information system 30, the medical image management system 40, and the report system 50. The communication interface 13 performs data communication in accordance with, for example, a preset known standard. For example, communication conforming to HL7 is carried out with the hospital information system 20. Further, for example, DICOM-compliant communication is carried out with the medical image management system 40.

The diagnostic support device 10 may have an input interface. The input interface receives various input operations from the user, converts the received input operations into electric signals, and outputs the received input operations to the processing circuit 11. The input interface is connected to, for example, an input device such as a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touch pad, and a touch panel on which instructions are input by touching an operation surface. Further, the input device connected to the input interface may be an input device provided in another computer connected via a network or the like.

Further, the diagnostic support device 10 may have a display. The display displays various information according to the instructions from the processing circuit 11. Further, the display may display a GUI (Graphical User Interface) or the like for accepting various operations from the user. As the display, any display such as a CRT (Cathode Ray Tube) display, a liquid crystal display, an organic EL display, an LED display, and a plasma display can be appropriately used.

The processing circuit 11 shown in FIG. 2 realizes a function corresponding to the diagnosis support program stored in the memory 12. For example, the processing circuit 11 executes an image search function 111, an area extraction function 112, a feature amount extraction function 113, an image information acquisition function 114, a correlation determination function 115, a finding creation function 116, and a report by executing a diagnosis support program. It has a creation function 117. In this embodiment, the image search function 111, the area extraction function 112, the feature amount extraction function 113, the image information acquisition function 114, the correlation determination function 115, the finding creation function 116, and the report creation function 117 are provided by a single processor. The case where it is realized will be described, but the present invention is not limited to this. For example, a processing circuit is configured by combining a plurality of independent processors, and each processor executes a program to execute an image search function 111, an area extraction function 112, a feature amount extraction function 113, an image information acquisition function 114, and a correlation determination function. 115, the finding creation function 116, and the report creation function 117 may be realized.

The image search function 111 is a function for searching an image necessary for image interpretation from an image stored in the medical image management system 40. Specifically, for example, in the image search function 111, the processing circuit 11 acquires the inspection purpose of the inspection requested to be read from the hospital information system 20 or the report system 50. The processing circuit 11 replaces the acquired inspection purpose with the image interpretation purpose, and acquires information about the image type associated with the image interpretation purpose.

The relationship between the purpose of image interpretation and the image type is associated with, for example, a relationship table stored in the memory 12. In the relation table, the image types judged by clinical findings that confirmation is required for the purpose of image interpretation are summarized in advance for each purpose of image interpretation. The relation table can be updated as needed. The update of the relation table may be carried out by the input of the operator, or may be carried out automatically by a predetermined learning system.

The processing circuit 11 accesses the medical image management system 40 and searches for an image file about the acquired image type from the image file created by the inspection. The processing circuit 11 transmits the searched image file to the image interpreting doctor via the communication interface 13.

The area extraction function 112 is a function of extracting a target area to be determined from a medical image. Specifically, for example, in the region extraction function 112, the processing circuit 11 acquires a method for extracting a target region for each image type when at least a part of a medical image is designated by an image interpreting doctor. The relationship between each image type and the method for extracting the target area for each of these image types is associated with, for example, a definition table stored in the memory 12. In the definition table, extraction methods capable of extracting an effective target area for each image type are summarized in advance for each diagnosis result. The definition table can be updated as needed. The definition table may be updated by input from the operator, or may be automatically updated by a predetermined learning system. The processing circuit 11 extracts a target area from the medical image of each image type based on the acquired extraction method.

The feature amount extraction function 113 is a function for extracting the feature amount for each image type for the extracted target area. Specifically, for example, in the feature amount extraction function 113, the processing circuit 11 acquires a method for extracting the feature amount of the extracted target region. The relationship between each image type and the method of extracting the feature amount for each of these image types is associated with, for example, a definition table stored in the memory 12. In the definition table, methods that can extract effective features for each image type are summarized in advance for each diagnostic result that can be determined. The processing circuit 11 extracts the feature amount for the target region by using the acquired method.

The image information acquisition function 114 is a function for acquiring information about a medical image designated by an image interpreting doctor. Specifically, for example, in the image information acquisition function 114, the processing circuit 11 acquires information on a medical image, that is, a key image, which is at least partially designated by an image interpreting doctor. The processing circuit 11 also acquires information about a medical image including a part identified as a part designated by the image interpreting doctor. The image information about the key image is, for example, link information, and includes, for example, an address for accessing the medical image stored in the medical image management system 40.

The correlation determination function 115 is a function for determining the correlation between medical images of each image type based on the feature amount. Specifically, for example, in the correlation determination function 115, the processing circuit 11 collates the extracted feature amount with a standard preset for each image type to obtain a correlation between medical images of each image type. judge. Criteria for the feature amount for determining the correlation between the medical images of each image type are set based on, for example, clinical findings, and are preliminarily summarized in a correlation table stored in the memory 12.

Further, in the correlation determination function 115, the processing circuit 11 acquires the diagnosis result associated with the correlation between the determined medical images. Specifically, for example, in the correlation table, a standard for a feature amount for determining a correlation between medical images of each image type is associated with a diagnosis result. The processing circuit 11 acquires the diagnosis result associated with the correlation between the determined medical images based on the correlation table. The correlation table can be updated as needed. The update of the correlation table may be carried out by the input of the operator, or may be carried out automatically by a predetermined learning system. In addition, when the correlation table is derived from the correlation between the medical images of each image type, for example, the diagnostic results judged by the clinical findings are summarized in advance for each interpretation purpose.

The finding creation function 116 is a function of referring to the determination result of the correlation determination function 115 and creating the finding content in the interpretation report. Specifically, for example, in the finding creation function 116, the processing circuit 11 creates the finding content based on the information registered in the correlation table and the like. The processing circuit 11 transmits the created findings to the image interpreting doctor via the communication interface 13.

The report creation function 117 is a function for creating an interpretation report based on the created findings. Specifically, for example, in the report creation function 117, the processing circuit 11 creates an image interpretation report based on the findings, the key image referred to when creating the findings, the image information about the key images, and the like. The processing circuit 11 transmits the created image interpretation report to the image interpretation doctor via the communication interface 13.

Next, the operation of the diagnostic support device 10 configured as described above will be described in detail according to the processing procedure of the processing circuit 11.

FIG. 3 is a diagram illustrating an example of processing when an image interpreting doctor creates an image interpretation report using the diagnosis support device 10 via a communication terminal 70. In the description of the process of the diagnostic support device 10 using FIG. 3, it is assumed that the test to be read is, for example, a “head MR test” and the purpose of the test is, for example, “cerebral infarction”. Further, with the inspection of the image-reading target being "head MR examination" and the examination purpose being "cerebral infarction", the memory 12 of the diagnostic support device 10 has the relationship table shown in FIG. 4 and FIG. It is assumed that the defined correlation table shown is stored.

FIG. 4 is a diagram showing an example of a relationship table in which an image interpretation purpose and an image type are associated with each other. According to FIG. 4, the purpose of image interpretation: "cerebral infarction", image type 1: "DWI", image type 2: "ADC map", and image type 3: which were judged to require confirmation by clinical findings. A "T2-weighted image" is associated with it. Further, for the purpose of image interpretation: "vascular stenosis", image type 1: "MRA X direction" and image type 2: "MRA Y direction", which are determined by clinical findings to require confirmation, are associated with each other. .. The relational table shown in FIG. 4 includes two image interpretation purposes and an image type associated with these image interpretation purposes, but the relationship table is not limited to this. The relationship table may contain other image interpretation purposes and image types associated with the image interpretation purpose. For example, in the relation table, the image type related to the image interpretation purpose: "bleeding" and the image interpretation purpose: "bleeding", and the image type related to the image interpretation purpose: "inflammation" and the image interpretation purpose: "inflammation". Etc. may be included.

FIG. 5 is a diagram showing an example of a definition correlation table used when creating an interpretation report on cerebral infarction. In the definition correlation table shown in FIG. 5, a definition table summarizing a method for extracting a target area and a method for extracting a feature amount and a correlation between medical images of each image type are determined. The criteria for the feature amount are described together with the correlation table summarized for each diagnosis result. According to FIG. 5, the diagnosis result for cerebral infarction represented by the stage and pathological condition is associated with the standard for the feature amount, and the definition for region extraction and the definition for feature amount extraction. There is. The criteria for the feature amount, the definition for region extraction, and the definition for feature amount extraction are set for each image type. In FIG. 5, in order to improve the determination accuracy of the diagnosis result, the diagnosis result for cerebral infarction is associated with the standard of the feature amount in the CT image and the method for extracting the region, but the CT image is Not always required.

Further, in FIG. 5, a definition correlation table for cerebral infarction is shown, but the definition correlation table is not limited to this. The memory 12 may also store a definition correlation table suitable for other inspection purposes. Further, in FIG. 5, the definition correlation table in which the definition table and the correlation table are integrated is shown, but the definition table and the correlation table may be stored in the memory 12 as separate tables. ..

In FIG. 3, the image interpreting doctor first accesses the report system 50 via the communication terminal 70, and selects "head MR examination", which is an examination to be interpreted, from the management list that manages the image interpretation order (step). S31). The selection signal for the “head MR examination” is transmitted from the communication terminal 70 to the diagnostic support device 10 (step S32).

Upon receiving the selection signal from the communication terminal 70, the processing circuit 11 of the diagnosis support device 10 executes the image search function 111. In the image search function 111, the processing circuit 11 acquires the inspection purpose for the selected “head MR inspection” from the hospital information system 20 (step S33). Specifically, for example, the server device 21 of the hospital information system 20 has an inspection purpose of "head MR examination" based on an instruction signal input from a medical doctor or the like via a communication terminal 22: "cerebral infarction". Is remembered. The processing circuit 11 reads out the inspection purpose: "cerebral infarction" from the server device 21. The processing circuit 11 may acquire the inspection purpose from the report system 50.

When the processing circuit 11 acquires the inspection purpose: "cerebral infarction", the processing circuit 11 converts the acquired inspection purpose: "cerebral infarction" into an image interpretation purpose, and causes the converted image interpretation purpose to be collated with the relation table stored in the memory 12. .. The processing circuit 11 is associated with the purpose of image interpretation: "cerebral infarction" in the relation table, image type 1: "DWI", image type 2: "ADC map", and image type 3: "T2 weighted image". (Step S34). Further, when the processing circuit 11 acquires the inspection purpose: "cerebral infarction", the processing circuit 11 reads out the definition correlation table set for "cerebral infarction" from the memory 12 (step S35).

The processing circuit 11 is a DWI, ADC map, and T2-weighted image corresponding to the image type acquired in step S34 from the medical image acquired by the inspection: “head MR examination” and stored in the medical image management system 40. Is searched (step S36). The processing circuit 11 reads out a plurality of DWIs, ADC maps, and T2-weighted images acquired as a result of the search from the medical image management system 40, and transmits them as medical image data to the communication terminal 70 (step S37). When the medical image management system 40 stores a CT image of the head of the same patient, the processing circuit 11 may also read the CT image and transmit it to the communication terminal 70 as medical image data.

Upon receiving the medical image data, the communication terminal 70 displays the DWI, ADC map, and T2-weighted image based on the received medical image data in the display layout for diagnosing "cerebral infarction" (step S38). The image interpreter refers to the DWI, ADC map, and T2-weighted image displayed on the communication terminal 70 to search for lesions in the image. When a site suspected to be a lesion is found in an image, that is, for example, when a site suspected to be a lesion is found in one DWI (hereinafter referred to as a key DWI) among a plurality of DWIs, the image interpreter finds the site. Annotate the site (step S39). The communication terminal 70 outputs the annotation information about the annotation attached by the image interpreter to the diagnosis support device 10 (step S310). When the CT image is included in the medical image data, the communication terminal 70 may also display the CT image in the display layout for diagnosing "cerebral infarction".

Upon receiving the annotation information, the processing circuit 11 of the diagnosis support device 10 executes the area extraction function 112. In the area extraction function 112, the processing circuit 11 extracts a target area to be determined from the annotated image or the like based on the received annotation information (step S311). Specifically, first, the processing circuit 11 acquires a method for region extraction from the definition correlation table acquired in step S35 based on the annotation information. For example, when a part of the key DWI is annotated, the processing circuit 11 acquires the "region" from the row of the definition for "DWI" in the definition correlation table. In the present embodiment, the “region” represents extracting pixel values in the vicinity of the annotated position. The range of the extracted pixel values differs depending on the focus point defined according to the type of annotation. If the annotation is arrow-shaped, the tip of the arrow will be the focus point. For example, when the key DWI is annotated with an arrow, the predetermined range at the tip of the arrow is the range from which the pixel value is extracted. The processing circuit 11 extracts pixel values in the range near the annotated position from the key DWI with a predetermined gradation. The processing circuit 11 stores the extracted pixel values in the memory 12.

As the region extraction method, an existing region extraction method such as bolus detection is used. In addition, another region extraction method may be used. Further, in the definition correlation table shown in FIG. 5, the case where it is described as "region" is shown, but the present invention is not limited to this. In the definition correlation table, for example, a region extraction method may be defined instead of the “region”. Further, in the definition correlation table, for example, in addition to the "region", the hue, the state, and the like may be defined.

Further, for example, when a part of the key DWI is annotated, the processing circuit 11 has a plurality of ADC maps and a T2-weighted image, an ADC map for the same portion as the key DWI, and a T2-weighted image. Are selected as the key ADC map and the key T2-weighted image, respectively. The processing circuit 11 identifies the part corresponding to the annotated part in the key DWI from the key ADC map and the key T2-weighted image. Identification of the site annotated by the key DWI and the corresponding site is performed using, for example, the physiological position of the patient such as Anatomical Landmark, the coordinates in the annotated DWI, and the like. The processing circuit 11 extracts pixel values in the range near the identified portion from the key ADC map and the key T2-weighted image with a predetermined gradation. The processing circuit 11 stores the extracted pixel values in the memory 12.

When the pixel value of the target region is extracted, the processing circuit 11 executes the feature amount extraction function 113. In the feature amount extraction function 113, the processing circuit 11 acquires the feature amount based on the extracted pixel value (step S312). Specifically, for example, the processing circuit 11 calculates the intensity of the detection signal of the image based on the pixel values extracted from the key DWI, the key ADC map, and the key T2-weighted image. The processing circuit 11 converts the signal intensity from, for example, the extracted image gradation. The conversion from the image gradation is performed, for example, by defining a linear function between the preset minimum value and the maximum value and substituting the extracted gradation into the defined linear function. In the conversion from the image gradation, a function specified by the user, for example, an nth-order function or the like may be used in addition to the linear function.

When the signal strength is calculated for the key DWI, the key ADC map, and the key T2-weighted image, the processing circuit 11 acquires a method for feature quantity extraction for the image for which the signal strength is calculated from the definition correlation table acquired in step S35. do. For example, when the signal strength is calculated for the key DWI, the processing circuit 11 acquires the "difference from the ADC" from the row of the definition for "DWI" in the definition correlation table. In the present embodiment, the "difference from the ADC" is, for example, the difference between the signal strength calculated for the DWI and the signal strength calculated by inverting the ADC map in the DWI and the ADC map representing the same part. Represents. The signal strength difference calculation method uses the existing signal strength difference calculation.

Subsequently, the processing circuit 11 acquires the "peripheral difference" from the row of the definition for "DWI" in the definition correlation table. In the present embodiment, the "peripheral difference" is calculated based on, for example, a pixel value extracted from a range near a site designated by an image interpreter or a range near a site identified as the site in the same image. It represents taking the difference between the signal strength obtained and the signal strength calculated based on the pixel values around this neighborhood range. The processing circuit 11 calculates the signal strength for the key DWI based on the acquired "difference from ADC" and "peripheral difference".

Subsequently, the processing circuit 11 acquires a "threshold value" for acquiring the feature amount from the row of the definition about "DWI" in the definition correlation table. For example, the definition of "DWI" defines, for example, a threshold value for determining a feature amount: "equal signal", "low signal", and "high signal". The processing circuit 11 acquires the feature amount of "equal signal", "low signal", or "high signal" corresponding to the calculated signal strength based on the acquired "threshold value". The processing circuit 11 stores the acquired feature amount in the memory 12.

Further, for example, when the signal strength is calculated for the key ADC map, the processing circuit 11 acquires the "difference from DWI" from the row of the definition for "ADC" in the definition correlation table. In the present embodiment, the "difference from the DWI" is, for example, the difference between the signal strength calculated for the ADC map and the signal strength calculated by inverting the DWI in the DWI and the ADC map representing the same part. Represents.

Subsequently, the processing circuit 11 acquires the "time difference" from the row of the definition for "ADC" in the definition correlation table. In the present embodiment, the "time difference" is, for example, the difference between the signal strength calculated for the latest image and the signal strength calculated for the past image in the latest image and the past image representing the same part. Represents that. The past image is acquired by referring to the interpretation report created in the past inspection, for example, as described later. The processing circuit 11 calculates the signal strength for the key ADC map based on the acquired “difference from DWI” and “difference over time”.

Subsequently, the processing circuit 11 acquires the feature amount: "no change", "decrease", or "increase" based on the calculated change in signal strength with time. The processing circuit 11 may acquire new generations and disappearances as feature quantities in addition to the feature quantities: "no change", "decrease", and "rise". The processing circuit 11 stores the acquired feature amount in the memory 12.

Further, for example, when the signal intensity is calculated for the key T2-weighted image, the processing circuit 11 acquires the "peripheral difference" from the definition row for "T2" in the definition correlation table. The processing circuit 11 calculates the signal strength for the key T2-weighted image based on the acquired "peripheral difference".

Subsequently, the processing circuit 11 acquires a "threshold value" for acquiring the feature amount from the row of the definition for "T2" in the definition correlation table. For example, in the definition of "T2", for example, a threshold value for determining a feature amount: "high signal" is defined. The processing circuit 11 acquires a feature amount: "high signal" for the calculated signal strength based on the acquired "threshold value". The processing circuit 11 stores the acquired feature amount in the memory 12.

Further, for example, when the signal strength is calculated for a CT image, the processing circuit 11 acquires a "threshold value" for acquiring a feature amount from the row of definitions for "CT" in the definition correlation table. For example, in the definition of "CT", for example, a CT value for determining a feature amount: "low absorption" is defined as a threshold value. The processing circuit 11 acquires a feature amount: "low absorption" for the calculated signal strength based on the acquired "threshold value". The processing circuit 11 stores the acquired feature amount in the memory 12.

When the processing circuit 11 acquires the feature amount, the processing circuit 11 executes the correlation determination function 115. When the correlation determination function 115 is executed, the processing circuit 11 determines the correlation between the key DWI, the key ADC map, and the key T2-weighted image by collating the acquired features with the criteria set in the definition correlation table. (Step S313). Then, the processing circuit 11 acquires the diagnostic result associated with the correlation between the key DWI, the key ADC map, and the key T2-weighted image (step S314). That is, as shown in FIG. 6, the processing circuit 11 acquires a diagnosis result by performing a correlation determination using the correlation table and the acquired feature amount.

Specific examples will be described with reference to FIGS. 7 to 12. For example, as shown in FIG. 7, it is assumed that the key DWI is annotated and the part corresponding to the annotation for the key DWI is identified from the key ADC map corresponding to the key DWI. In the key T2-weighted image, the lesion cannot be confirmed. At this time, the processing circuit 11 acquires the feature amount: "high signal" for the key DWI, the feature amount: "decrease" for the key ADC map, and the feature amount: for the key T2-weighted image. Get "deemed". The processing circuit 11 compares the acquired features with the definition correlation table, and as shown in FIG. 8, features 1: “high signal” for DWI, feature 2: “decrease” for ADC, and T2. Feature 3: "Deemed", stage: "hyperacute stage (1-24 hours)", and pathology: "cellular edema" are obtained as diagnostic results. The processing circuit 11 stores the stage: "hyperacute stage (1-24 hours)" and the condition: "cellular edema" in the memory 12 as a diagnosis result.

Further, for example, as shown in FIG. 9, it is assumed that the key DWI is annotated and the part corresponding to the annotation for the key DWI is identified from the key ADC map corresponding to the key DWI. In the key T2-weighted image, the lesion cannot be confirmed. At this time, the processing circuit 11 acquires the feature amount: "high signal" for the key DWI, the feature amount: "decrease" for the key ADC map, and the feature amount: for the key T2-weighted image. Get "deemed". The processing circuit 11 compares the acquired features with the definition correlation table, and as shown in FIG. 8, features 1: “high signal” for DWI, feature 2: “decrease” for ADC, and T2. Feature 3: "Deemed", stage: "hyperacute stage (1-24 hours)", and pathology: "cellular edema" are obtained as diagnostic results.

Further, for example, as shown in FIG. 10, it is assumed that the key DWI is annotated and the part corresponding to the annotation for the key DWI is identified from the key ADC map corresponding to the key DWI and the key T2-weighted image. At this time, the processing circuit 11 acquires the feature amount: "high signal" for the key DWI, the feature amount: "decrease" for the key ADC map, and the feature amount: for the key T2-weighted image. Get "high signal". The processing circuit 11 compares the acquired features with the definition correlation table, and as shown in FIG. 11, features 1: “high signal” for DWI, feature 2: “decrease” for ADC, and T2. Feature 3: "High signal", stage: "acute stage (1-7 days)", and pathology: "cellular edema + angioedema" are acquired as diagnostic results.

When the processing circuit 11 acquires the diagnosis result, the processing circuit 11 executes the finding creation function 116. When the finding creation function 116 is executed, the processing circuit 11 has the acquired diagnostic result, the annotated physiological position, the protocol name / series description used when acquiring the diagnostic result, as shown in FIG. And the content of the findings is created based on the feature amount acquired by this protocol (step S315).

Specifically, for example, the annotated physiological position is recognized as the "left frontal lobe", and as shown in FIG. 13, the diagnosis result is the stage: "hyperacute stage (1-24 hours)". And pathological condition: "Cell edema" was acquired, the protocol name and series description used for the judgment were "DWI, ADC, T2, CT", and the feature quantities acquired by this protocol were "high signal" respectively. In the case of "Early sign", the processing circuit 11 "has a high signal in the DWI in the left frontal lobe, the ADC map is degraded, the T2-weighted image is regarded, and the CT image has an Early sign". Therefore, cellular edema is suspected, and it is considered to be a cerebral infarction in the hyperacute stage. " The processing circuit 11 may create the content of the findings by using, for example, a method such as a structured report. The processing circuit 11 transmits the created finding content to the communication terminal 70 as finding data (step S316).

Upon receiving the finding data, the communication terminal 70 displays the finding content based on the finding data on the display (step S317). The interpreting doctor confirms the content of the findings displayed on the communication terminal 70, and corrects the content of the findings as necessary (step S318). For example, the interpreter removes unnecessary and redundant information in the findings. As a result, the findings created by the diagnostic support device 10: "There is a high signal in the DWI in the left frontal lobe, the ADC map is degraded, it is regarded as a T2-weighted image, and there is an Early sign in the CT image. "Sexual edema is suspected and is considered to be a hyperacute cerebral infarction." Is rewritten as, for example, "There is a high signal in the DWI in the left frontal lobe and it is considered to be a hyperacute cerebral infarction." When the correction of the findings by the image interpreting doctor is completed, the communication terminal 70 transmits the corrected findings to the diagnosis support device 10 as the findings data.

Upon receiving the corrected finding data, the processing circuit 11 executes the image information acquisition function 114. When the image information acquisition function 114 is executed, the processing circuit 11 acquires the link information about the key DWI, the key ADC map, and the key T2-weighted image referred to when creating the finding content as image information (step S320).

When the processing circuit 11 acquires the link information about the key DWI, the key ADC map, and the key T2-weighted image, the processing circuit 11 executes the report creation function 117. When the report creation function 117 is executed, the processing circuit 11 creates an image interpretation report based on the corrected findings (step S321). Specifically, for example, the processing circuit 11 writes an examination date, an examination purpose, patient information, and the like in a predetermined report format. The processing circuit 11 reflects the corrected findings at a predetermined position in the report format. The processing circuit 11 attaches the key DWI, the key ADC map, and the key T2-weighted image referred to when creating the findings. Then, the processing circuit 11 associates the key DWI, the key ADC map, and the key T2-weighted image with the link information attached to the key DWI, the key ADC map, and the key T2-weighted image, respectively. This will provide hyperlinks to the key DWI, key ADC map, and key T2-weighted image. The processing circuit 11 transmits the created image interpretation report as report data to the communication terminal 70 (step S322).

Upon receiving the report data, the communication terminal 70 displays an image interpretation report based on the report data on the display (step S323). The image interpreting doctor confirms the image interpretation report displayed on the communication terminal 70, and determines whether or not the inspection purpose for which the findings should be described remains. If it remains, the image interpreting doctor designates the next examination purpose to the diagnosis support device 10, and causes the diagnosis support device 10 to repeat the process from step S34. If there is no examination purpose to describe the findings, the image interpreter completes the preparation of the image interpretation report (step S324). When the image interpretation report is created by the image interpretation doctor, the communication terminal 70 transmits the report data to the report system 50. The processing circuit of the communication terminal 70 may determine whether or not the inspection purpose for which the findings should be described remains. If the inspection remains, the communication terminal 70 designates the next inspection purpose to the diagnosis support device 10 and causes the diagnosis support device 10 to repeat the process from step S34.

As described above, in the first embodiment, the diagnostic support device 10 acquires the purpose of the examination when the image interpreting doctor designates the examination. The diagnostic support device 10 acquires an image type associated with the inspection purpose. The diagnosis support device 10 displays the medical image of the acquired image type among the medical images acquired by the examination to the image interpreting doctor. The diagnosis support device 10 extracts the feature amount of the portion designated by the image interpreting doctor from the displayed medical image. Then, the diagnosis support device 10 acquires the diagnosis result associated with the feature amount extracted for each image type. As a result, the image interpreting doctor only needs to confirm the medical image of the image type necessary for the diagnosis, so that the time devoted to the search of the medical image can be suppressed.

Further, in the first embodiment, when the image interpreting doctor designates a site for one of the displayed medical images, the diagnostic support device 10 has the same position as the medical image, and the image type is Select a different medical image. The diagnostic support device 10 identifies the same site as the site designated by the image interpreter in the selected medical image. Then, the diagnosis support device 10 extracts the feature amount between the site designated by the image interpreting doctor and the identified site. As a result, it is possible to acquire the feature amount for this part and the feature amount for the part of another image type corresponding to this part only by specifying one part of the displayed medical image. It becomes.

Further, in the first embodiment, the diagnosis support device 10 creates a finding content based on the diagnosis result, the image type referred to when acquiring the diagnosis result, and the feature amount extracted from the medical image of this image type. I try to do it. As a result, the image interpreting doctor can acquire the content of the findings only by designating the part of the displayed medical image.

Further, in the first embodiment, the diagnostic support device 10 reflects the findings corrected by the image interpreting doctor in a predetermined format, and attaches the medical image referred to when the findings are created to the format as a key image. , By embedding the image information about the key image in the format, the interpretation report is created. That is, the evidence association processing of the diagnosis result is automatically executed. As a result, the image interpreting doctor can obtain an image interpretation report in which the comprehensive judgment for the examination purpose is described only by designating the part of the displayed medical image and correcting the content of the findings.

In the first embodiment, a case where a relation table, a definition table, and a correlation table are stored in the memory 12 and necessary information is acquired by referring to these tables has been described as an example. However, it is not limited to this. Instead of the relation table, the memory 12 may store a model trained to provide an image type that needs to be confirmed when an image interpretation purpose is input. Further, in the memory 12, instead of the definition table, when a medical image of a predetermined image type is input, a model trained to extract a feature amount corresponding to the input medical image is stored. May be good. Further, instead of the correlation table, the memory 12 may store a model trained to output a diagnostic result according to the input feature amount when a predetermined feature amount is input.

Further, in the first embodiment, the case of creating an image interpretation report for MR inspection has been described as an example. The inspection for which the interpretation report is created is not limited to the MR inspection. The inspection for which the interpretation report is to be created may be, for example, an inspection by dual energy imaging using an X-ray CT apparatus, photon counting CT imaging, or the like.

Further, in the first embodiment, the case where the processing circuit 11 uses the image search function 111 to refer to, for example, the relational table shown in FIG. 4 and acquire a plurality of related types of images is described. However, the data defined in the relation table is not limited to images. Information on past inspections, such as interpretation reports, may be defined in the relation table. When the information about the past examination is defined in the relation table, the processing circuit 11 of the diagnostic support device 10 operates as follows, for example.

The processing circuit 11 collates a predetermined interpretation purpose with the relation table stored in the memory 12, and acquires, for example, "T2-weighted image" and "past interpretation report" defined in the relation table. do. The processing circuit 11 reads out the image interpretation report created by the past examination for the same patient corresponding to the acquired "past image interpretation report" from the hospital information system 20, the radiation department information system 30, and the like. In the read interpretation report, for example, there is a description that "in the T2-weighted image, there is old-fashioned bleeding in the left putamen." Further, the T2-weighted image referred to by the image-reading doctor when inputting this finding is attached to the interpretation report as a key T2-weighted image (key image). When the image interpretation report is read, the processing circuit 11 extracts the key image attached to the image interpretation report. If the key image is not attached to the image interpretation report, the processing circuit 11 may read the image acquired in the past inspection from the medical image management system 40.

Further, the processing circuit 11 reads out the medical image data corresponding to the "T2-weighted image" acquired by referring to the related table from the medical image management system 40. The processing circuit 11 transmits the medical image data of the key image extracted from the image interpretation report and the medical image data read from the medical image management system 40 to the communication terminal 70.

When the communication terminal 70 receives the medical image data, the communication terminal 70 displays an image based on the received medical image data, for example, a T2-weighted image for the current inspection and a T2-weighted image for the past inspection in the display layout. In the following description, the T2-weighted image for the current inspection is referred to as a present T2-weighted image, and the T2-weighted image for the past inspection is referred to as a past T2-weighted image. The image interpreting doctor refers to the current T2-weighted image displayed on the communication terminal 70 and the past T2-weighted image, and searches for a lesion in the image. If a site currently suspected to be a lesion is found on a T2-weighted image, the interpreter will annotate the site. The communication terminal 70 outputs the annotation information about the annotation attached by the image interpreter to the diagnosis support device 10.

Upon receiving the annotation information, the processing circuit 11 of the diagnosis support device 10 executes the area extraction function 112. In the area extraction function 112, the processing circuit 11 extracts a target area to be determined for an annotated image or the like based on the received annotation information. The processing circuit 11 extracts pixel values in the range near the annotated position from the currently T2-weighted image with a predetermined gradation.

Further, when a part of the present T2-weighted image is annotated, the processing circuit 11 identifies a portion corresponding to the annotated portion in the current T2-weighted image from the past T2-weighted image. The processing circuit 11 extracts pixel values in the range near the identified portion from the past T2-weighted image with a predetermined gradation. The processing circuit 11 stores the extracted pixel values in the memory 12.

When the pixel value of the target region is extracted, the processing circuit 11 executes the feature amount extraction function 113. In the feature amount extraction function 113, the processing circuit 11 acquires the feature amount based on the extracted pixel value. Specifically, for example, the processing circuit 11 calculates the intensity of the detection signal of the image based on the pixel values extracted from the current T2-weighted image and the past T2-weighted image. The processing circuit 11 converts the signal intensity from, for example, the extracted image gradation.

When the signal strength of the current T2-weighted image and the signal strength of the past T2-weighted image are acquired, the processing circuit 11 compares the acquired signal strength. Specifically, for example, the difference (difference in gradation) between the signal intensity calculated for the current T2-weighted image and the signal intensity calculated for the past T2-weighted image is taken. The processing circuit 11 determines whether or not the calculated difference in signal strength exceeds a plurality of preset threshold values. Here, the plurality of threshold values are, for example, the threshold values for determining the feature amount of the change of the current T2-weighted image with respect to the past T2-weighted image: "increase", "decrease", or "no change".

When the feature amount of the change of the present T2-weighted image with respect to the past T2-weighted image is acquired, the processing circuit 11 executes the correlation determination function 115. When the correlation determination function 115 is executed, the processing circuit 11 determines the correlation between the current T2-weighted image and the past T2-weighted image based on the acquired features. Then, the processing circuit 11 acquires the diagnostic result associated with the correlation between the current T2-weighted image and the past T2-weighted image.

When the processing circuit 11 acquires the diagnosis result, the processing circuit 11 executes the finding creation function 116. When the finding creation function 116 is executed, the processing circuit 11 creates the finding content based on the acquired diagnosis result, the annotated physiological position, the read past interpretation report, and the acquired feature amount. Specifically, for example, when the processing circuit 11 determines that the feature amount of the change in the current T2-weighted image with respect to the past T2-weighted image is "no change", "old bleeding on the left putamen, previous T2-weighted image". Acquire the content of the finding that "there is no change with the image".

The processing circuit 11 transmits the created findings to the communication terminal 70 as finding data. Upon receiving the finding data, the communication terminal 70 displays the finding content based on the finding data on the display. The interpreting doctor confirms the content of the findings displayed on the communication terminal 70, and corrects the content of the findings as necessary. For example, the content of the findings created by the diagnostic support device 10: "old bleeding in the left putamen, no change from the previous T2-weighted image" is "no significant change from the previous old bleeding in the left putamen." Is rewritten as. When the correction of the findings by the image interpreting doctor is completed, the communication terminal 70 transmits the corrected findings to the diagnosis support device 10 as the findings data. As a result, the content of the findings considering the difference over time is created.

(Second embodiment)
In the first embodiment, the case where the diagnostic support device 10 is housed in the system in the hospital has been described as an example. In the second embodiment, a case where the diagnosis support device 10a is provided outside the hospital will be described as an example.

FIG. 14 is a block diagram showing a configuration example of a diagnostic support system provided with the diagnostic support device 10a according to the second embodiment. The diagnosis support device 10a shown in FIG. 14 is connected to the hospital information system via the Internet and a network such as a communication network provided by a telecommunications carrier. The diagnosis support device 10a may be realized by one server device provided on the network, or may be realized by a plurality of server devices.

FIG. 15 is a block diagram showing an example of the functional configuration of the diagnostic support device 10a shown in FIG. The diagnostic support device 10a shown in FIG. 15 has a processing circuit 11a, a memory 12, and a communication interface 13. The processing circuit 11a, the memory 12, and the communication interface 13 are connected to each other so as to be able to communicate with each other via, for example, a bus.

The processing circuit 11a is a processor that functions as the center of the diagnostic support device 10a. The processing circuit 11a realizes a function corresponding to the diagnosis support program by executing the diagnosis support program stored in the memory 12 or the like. For example, the processing circuit 11a executes an image search function 111a, an area extraction function 112, a feature amount extraction function 113, an image information acquisition function 114a, a correlation determination function 115, a finding creation function 116, and a report by executing a diagnosis support program. It has a creation function 117. In this embodiment, the image search function 111a, the area extraction function 112, the feature amount extraction function 113, the image information acquisition function 114a, the correlation determination function 115, the finding creation function 116, and the report creation function 117 are provided by a single processor. The case where it is realized will be described, but the present invention is not limited to this. For example, a processing circuit is configured by combining a plurality of independent processors, and each processor executes a program to execute an image search function 111a, an area extraction function 112, a feature amount extraction function 113, an image information acquisition function 114a, and a correlation determination function. 115, the finding creation function 116, and the report creation function 117 may be realized.

The image search function 111a is a function for searching an image necessary for image interpretation from a plurality of transmitted images. Specifically, for example, in the image search function 111a, the processing circuit 11a accepts an input for inspection purposes for inspection for which image interpretation is requested. The processing circuit 11a replaces the input inspection purpose with the image interpretation purpose, and acquires information about the image type associated with the image interpretation purpose.

The relationship between the purpose of image interpretation and the image type is associated with, for example, a relationship table stored in the memory 12. The processing circuit 11a searches for a medical image about the acquired image type from a plurality of medical images for one examination transmitted from the client via the communication terminal 70 or the communication terminal 80. The processing circuit 11a transmits the searched medical image to the client via the communication interface 13.

The image information acquisition function 114a is a function for acquiring information about a medical image designated by the client. Specifically, for example, in the image information acquisition function 114a, the processing circuit 11a acquires information on a medical image, that is, a key image, at least partially designated by the client. The processing circuit 11a also acquires information about a medical image including a portion identified as a portion designated by the client. The acquired image information is, for example, link information, and includes, for example, an address for accessing a medical image stored in the memory 12 of the diagnostic support device 10a.

As described above, in the second embodiment, the diagnosis support device 10a is provided on the so-called cloud. The diagnostic support device 10a receives a medical image for a predetermined examination as well as a request for creating an image interpretation report from the communication terminals 70 and 80 connected to the network. The diagnosis support device 10a receives an input for inspection purposes from the client. The diagnostic support device 10a acquires an image type associated with the inspection purpose. The diagnosis support device 10a displays the medical image of the acquired image type among the received plurality of medical images to the client. The diagnosis support device 10a extracts the feature amount of the portion designated by the client from the displayed medical image. Then, the diagnosis support device 10a acquires the diagnosis result associated with the feature amount extracted for each image type. As a result, the client only needs to confirm the medical image of the image type necessary for the diagnosis by inputting the purpose of the examination, so that the time devoted to the search for the medical image can be suppressed.

According to at least one embodiment described above, the diagnostic support device can reduce the burden of diagnosis in image interpretation.

The word "processor" used in the description of the embodiment is, for example, a CPU (central processing unit), a GPU (Graphics Processing Unit), an integrated circuit for a specific application (Application Specific Integrated Circuit (ASIC)), or a programmable logic device. (For example, it means a circuit such as a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)). The processor realizes the function by reading and executing the program stored in the storage circuit. Instead of storing the program in the storage circuit, the program may be directly embedded in the circuit of the processor. In this case, the processor realizes the function by reading and executing the program embedded in the circuit. It should be noted that each processor of each of the above embodiments is not limited to the case where each processor is configured as a single circuit, and a plurality of independent circuits are combined to form one processor to realize its function. May be good. Further, a plurality of components in each of the above embodiments may be integrated into one processor to realize the function.

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other embodiments, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and variations thereof are included in the scope of the invention described in the claims and the equivalent scope thereof, as are included in the scope and gist of the invention.

10, 10a ... Diagnosis support device 11, 11a ... Processing circuit 12 ... Memory 13 ... Communication interface 20 ... Hospital information system 21 ... Server device 22 ... Communication terminal 30 ... Radiation department information system 31 ... Server device 32 ... Communication terminal 40 ... Medical Image management system 41 ... Server device 50 ... Report system 51 ... Server device 60 ... Medical image diagnostic device 70 ... Communication terminal 80 ... Communication terminal 111, 111a ... Image search function 112 ... Area extraction function 113 ... Feature quantity extraction function 114, 114a … Image information acquisition function 115… Correlation judgment function 116… Finding creation function 117… Report creation function

Claims (7)

An image search unit that acquires an examination purpose corresponding to a specified examination and searches for a medical image for an image type associated with the examination purpose from a plurality of medical images acquired in the examination.
A feature amount extraction unit that extracts the feature amount of the designated part from the medical image obtained by the above search, and a feature amount extraction unit.
A correlation determination unit that acquires diagnostic results derived from the medical image of the image type based on the feature amount extracted from the medical image of the image type .
A finding creation unit that creates findings based on the diagnosis result, the image type referred to when acquiring the diagnosis result, and the feature amount extracted from the medical image of the image type.
A diagnostic support device equipped with. The image search unit refers to an image associated with the inspection purpose by referring to a relationship table in which the image interpretation purpose derived from the inspection purpose and the image type for which confirmation is required for the image interpretation purpose are associated with each other. Get the seeds ,
The diagnostic support device according to claim 1. When a site is designated for one of the first medical images among the plurality of medical images obtained by the search, the feature amount extraction unit is at the same position as the first medical image, and the image type is different. A second medical image is selected, and in the selected second medical image, the same site as the site designated for the first medical image is identified, and the designated site and the identified site are used. Extract feature quantities ,
The diagnostic support device according to claim 1 or 2. The correlation determination unit refers to the correlation table in which the reference for the feature amount and the diagnosis result are associated with each other, and based on the feature amount extracted from the medical image of the image type, the diagnosis result. To get ,
The diagnostic support device according to any one of claims 1 to 3. By reflecting the created findings in a predetermined format, pasting the medical image referred to when the findings were created in the format as a key image, and embedding the image information about the key image in the format. It also has a report creation unit that creates interpretation reports .
The diagnostic support device according to any one of claims 1 to 4 . The image search means acquires the inspection purpose corresponding to the specified inspection and
A medical image for an image type associated with the examination purpose is searched from a plurality of medical images acquired in the examination.
The feature amount extraction means extracts the feature amount of the designated part from the medical image obtained by the above search, and then extracts the feature amount.
The correlation determination means obtains a diagnostic result derived from the medical image of the image type based on the feature amount extracted from the medical image of the image type.
The finding creating means creates the finding content based on the diagnosis result, the image type referred to when the diagnosis result is acquired, and the feature amount extracted from the medical image of the image type.
Diagnosis support method. A process of acquiring an examination purpose corresponding to a specified examination and searching for a medical image for an image type associated with the examination purpose from a plurality of medical images acquired in the examination.
The process of extracting the feature amount of the specified part from the medical image obtained by the above search, and
A process of acquiring a diagnostic result derived from a medical image of the image type based on a feature amount extracted from the medical image of the image type, and a process of acquiring the diagnosis result .
A process of creating findings based on the diagnosis result, the image type referred to when the diagnosis result is acquired, and the feature amount extracted from the medical image of the image type.
A diagnostic support program that causes the processor to execute.

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