Medical image analysis device, medical image analysis method, and program

Abstract

Provided are a medical image analysis device, a medical image analysis method, and a program for identifying a region of interest intended by a physician in a medical image from which a key image has been created. The problem is solved by a medical image analysis device comprising at least one processor and at least one memory that stores instructions to be executed by the at least one processor. The at least one processor acquires a key image that is created from a medical image and includes a region of interest, analyzes the key image to extract association information associating the key image with the medical image from which the key image has been created, and identifies the region of interest in the medical image on the basis of the association information.

Description

Medical image analysis device, medical image analysis method and program

[0001] The present invention relates to a medical image analysis device, a medical image analysis method, and a program, and in particular to a technique for utilizing key images in training a learning model.

[0002] Hospitals have a large number of key images created when doctors interpret medical images. A key image is a representative image that indicates a region of interest. The region of interest may be, for example, a lesion. When the original image is a three-dimensional medical image such as a CT image or an MRI image, the key image may be a slice of the region of interest, a slice that has been further cropped and saved, or a region of interest that has been annotated with a rectangle or an arrow and saved.

[0003] There are cases where the positional relationship between the key image and the original image is lost when the key image is created, and cases where image information is missing due to the addition of annotations or the like when the key image is created.

[0004] Patent Literature 1 discloses a technique for acquiring a key image, acquiring a cross-sectional image parallel to the key image, and generating a supplementary image. Patent Literature 2 discloses a technique for analyzing the key image, separating it into a medical image and an annotation image, and acquiring medical image information corresponding to the medical image.

[0005] JP 2020-28583 A JP 2015-156898 A

[0006] Since a large amount of data is required to train a deep learning model, it is possible to utilize these key images. However, there is a problem in that it is unclear which position in the original image the key image corresponds to, for example, it is unclear which slice and position it was created from. Furthermore, in cases where the key image has no annotations or is only annotated with an arrow, there is also the problem that the computer cannot identify the region of interest intended by the doctor.

[0007] The present invention has been made in consideration of these circumstances, and aims to provide a medical image analysis device, a medical image analysis method, and a program for identifying the region of interest intended by a doctor in the medical image from which a key image was created.

[0008] In order to achieve the above object, a medical image analysis device according to a first aspect of the present disclosure includes at least one processor and at least one memory that stores instructions to be executed by the at least one processor, wherein the at least one processor acquires a key image created from a medical image, the key image including a region of interest, analyzes the key image to extract linking information with the medical image from which the key image was created, and identifies the region of interest in the medical image based on the linking information. According to this aspect, the region of interest intended by a doctor can be identified in the medical image from which the key image was created, and the medical image with the identified region of interest can be used as training data for a learning model that estimates a region of interest from a medical image.

[0009] A medical image analysis apparatus according to a second aspect of the present disclosure is preferably a medical image analysis apparatus according to the first aspect, wherein at least one processor estimates a region of interest from a key image and adds the estimated region of interest to the medical image.

[0010] A medical image analysis device according to a third aspect of the present disclosure is preferably a medical image analysis device according to the first or second aspect, wherein the key image includes annotations indicating regions of interest, and at least one processor adds annotations to the medical image and identifies the regions of interest in the medical image based on the added annotations.

[0011] A medical image analysis apparatus according to a fourth aspect of the present disclosure is preferably the medical image analysis apparatus according to the third aspect, wherein at least one processor detects annotations from the key image.

[0012] In the medical image analysis device according to the fifth aspect of the present disclosure, in the medical image analysis device according to any one of the first to fourth aspects, it is preferable that the medical images include at least one of two-dimensional still images, three-dimensional still images, and moving images.

[0013] A medical image analysis device according to a sixth aspect of the present disclosure is a medical image analysis device according to any one of the first to fifth aspects, wherein the key image is preferably the result of volume rendering created from the medical image.

[0014] A medical image analysis device according to a seventh aspect of the present disclosure is a medical image analysis device according to any one of the first to sixth aspects, wherein at least one processor analyzes characters in the key image by character recognition to extract linking information, and the linking information preferably includes at least one of the window width, window level, slice number, and series number of the key image.

[0015] A medical image analysis device according to an eighth aspect of the present disclosure is a medical image analysis device according to any one of the first to seventh aspects, wherein at least one processor performs image recognition on a key image to extract linking information, and the linking information preferably includes at least one of a window width, a window level, and an annotation of the key image.

[0016] In a medical image analysis device according to a ninth aspect of the present disclosure, in a medical image analysis device according to any one of the first to eighth aspects, it is preferable that at least one processor extracts linking information from the result of alignment between the medical image and the key image.

[0017] In a medical image analysis device according to a tenth aspect of the present disclosure, in a medical image analysis device according to any of the first to ninth aspects, it is preferable that at least one processor estimates the corresponding position of a key image in the medical image based on the linking information.

[0018] In a medical image analysis apparatus according to an eleventh aspect of the present disclosure, in a medical image analysis apparatus according to any one of the first to tenth aspects, it is preferable that the region of interest is at least one of a mask, a bounding box, and a heat map.

[0019] A medical image analysis device according to a twelfth aspect of the present disclosure is the medical image analysis device according to any one of the first to eleventh aspects, wherein the medical images are preferably DICOM (Digital Imaging and Communications in Medicine) images.

[0020] In order to achieve the above object, a medical image analysis method according to a thirteenth aspect of the present disclosure is a medical image analysis method including: acquiring a key image created from a medical image, the key image including a region of interest; analyzing the key image to extract linking information between the key image and the medical image from which the key image was created; and identifying the region of interest in the medical image based on the linking information. According to this aspect, it is possible to identify the region of interest intended by a doctor in the medical image from which the key image was created, and therefore the medical image can be used as training data for a learning model.

[0021] In order to achieve the above object, a program according to a fourteenth aspect of the present disclosure is a program that causes a computer to execute the medical image analysis method of aspect 13. A non-transitory computer-readable recording medium, such as a CD-ROM (Compact Disk-Read Only Memory), that stores the program according to the fourteenth aspect is also included in the present disclosure.

[0022] According to the present invention, it is possible to identify a region of interest intended by a doctor in a medical image from which a key image is created.

[0023] FIG. 1 is a diagram showing the overall configuration of a medical image analysis system. FIG. 2 is a block diagram showing the electrical configuration of a medical image analysis device. FIG. 3 is a block diagram showing the functional configuration of a medical image analysis device. FIG. 4 is a flowchart showing a medical image analysis method according to a first embodiment. FIG. 5 is a diagram showing a key image and a medical image from which the key image was created. FIG. 6 is a flowchart showing a medical image analysis method according to a second embodiment. FIG. 7 is a diagram showing an example of a key image. FIG. 8 is a flowchart showing a medical image analysis method according to a third embodiment.

[0024] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0025] <Medical Image Analysis System> The medical image analysis system according to this embodiment is a system for identifying a region of interest in an original medical image from a key image created by a doctor. The original medical image from which the region of interest has been identified can be used as training data for a learning model.

[0026] Fig. 1 is a diagram showing the overall configuration of a medical image analysis system 10. As shown in Fig. 1, the medical image analysis system 10 is configured to include a medical image inspection device 12, a medical image database 14, a user terminal device 16, an image interpretation report database 18, and a medical image analysis device 20.

[0027] The medical image inspection equipment 12, medical image database 14, user terminal device 16, image interpretation report database 18, and medical image analysis device 20 are connected to each other via a network 22 so as to be able to transmit and receive data. The network 22 includes a wired or wireless local area network (LAN) that connects various devices within the medical institution for communication. The network 22 may also include a wide area network (WAN) that connects the LANs of multiple medical institutions.

[0028] The medical imaging inspection device 12 is an imaging device that captures an image of an area to be inspected of a subject and generates a medical image. Examples of the medical imaging inspection device 12 include an X-ray imaging device, a CT (Computed Tomography) device, an MRI (Magnetic Resonance Imaging) device, a PET (Positron Emission Tomography) device, an ultrasound device, a CR (Computed Radiography) device using a flat panel X-ray detector, and an endoscope device.

[0029] The medical image database 14 is a database that manages medical images captured by the medical image inspection equipment 12. The medical image database 14 is implemented by a computer equipped with a large-capacity storage device for storing medical images. Software that provides the functions of a database management system is installed in the computer.

[0030] The medical image may be a two-dimensional or three-dimensional still image taken by an X-ray imaging device, a CT device, an MRI device, or the like, or may be a moving image taken by an endoscope device.

[0031] The format of a medical image can be based on the Dicom (Digital Imaging and Communications in Medicine) standard. Supplementary information (Dicom tag information) defined in the Dicom standard may be added to the medical image. Note that the term "image" in this specification includes not only the image itself, such as a photograph, but also image data, which is a signal representing the image.

[0032] The user terminal device 16 is a terminal device used by a doctor to create and view an interpretation report. The user terminal device 16 is, for example, a personal computer. The user terminal device 16 may be a workstation or a tablet terminal. The user terminal device 16 includes an input device 16A and a display 16B. The doctor uses the input device 16A to input instructions for displaying medical images. The user terminal device 16 displays the medical images on the display 16B. Furthermore, the doctor interprets the medical images displayed on the display 16B, creates a key image from the medical image using the input device 16A, and inputs a statement of findings, which are the interpretation results, to create an interpretation report.

[0033] A key image is an image into which doctor information has been input. A key image is an image that is linked to the original medical image at the patient and imaging date and time level, but has lost information about its positional relationship with the original medical image. A key image may be an image that has been converted from the original medical image into an image such as a bitmap image, resulting in a loss of information compared to the original medical image, or an image that has been converted into an image that does not lose information. A key image may be an image in which image information at the position where annotations were added has been lost from the image information of the original medical image. A key image may also be the result of volume rendering created from a medical image.

[0034] The key image includes a region of interest that the physician is interested in. The key image may include an annotation indicating the region of interest. The annotation on the key image may be at least one of a circle, a rectangle, an arrow, a line segment, a point, and a scribble.

[0035] The key image may include text information, which may include at least one of a window width, a window level, a slice number, and a series number of the key image.

[0036] The image interpretation report database 18 is a database that manages image interpretation reports generated by users on the user terminal device 16. The image interpretation reports include key images. The image interpretation report database 18 is implemented by a computer equipped with a large-capacity storage device for storing image interpretation reports. Software that provides the functions of a database management system is installed in the computer. The medical image database 14 and the image interpretation report database 18 may be configured on a single computer.

[0037] The medical image analysis device 20 is a device that identifies a region of interest in a medical image. A personal computer or a workstation (an example of a "computer") can be used as the medical image analysis device 20. FIG. 2 is a block diagram showing the electrical configuration of the medical image analysis device 20. As shown in FIG. 2, the medical image analysis device 20 includes a processor 20A, a memory 20B, and a communication interface 20C.

[0038] The processor 20A executes instructions stored in the memory 20B. The hardware structure of the processor 20A is various processors as shown below. The various processors include a CPU (Central Processing Unit), which is a general-purpose processor that executes software (programs) and functions as various functional units, a GPU (Graphics Processing Unit), which is a processor specialized for image processing, a PLD (Programmable Logic Device), which is a processor whose circuit configuration can be changed after manufacture such as an FPGA (Field Programmable Gate Array), and a dedicated electrical circuit, such as an ASIC (Application Specific Integrated Circuit), which is a processor having a circuit configuration designed specifically for executing specific processing.

[0039] A single processing unit may be configured with one of these various processors, or may be configured with two or more processors of the same or different types (e.g., multiple FPGAs, a combination of a CPU and an FPGA, or a combination of a CPU and a GPU). Multiple functional units may also be configured with a single processor. Examples of multiple functional units configured with a single processor include: a first configuration, as typified by a client or server computer, in which a single processor is configured with a combination of one or more CPUs and software, and this processor operates as multiple functional units; and a second configuration, as typified by a SoC (System on Chip), in which a processor is used to realize the functions of an entire system including multiple functional units on a single IC (Integrated Circuit) chip. In this way, the various functional units are configured with one or more of the above-mentioned various processors as a hardware structure.

[0040] Furthermore, the hardware structure of these various processors is, more specifically, an electric circuit made up of a combination of circuit elements such as semiconductor elements.

[0041] The memory 20B stores instructions to be executed by the processor 20A. The memory 20B includes a random access memory (RAM) and a read-only memory (ROM), not shown. The processor 20A uses the RAM as a working area and executes software using various programs and parameters, including a medical image analysis program (described later), stored in the ROM, and also executes various processes of the medical image analysis device 20 by using the parameters stored in the ROM, etc.

[0042] The communication interface 20C controls communication with the medical image examination equipment 12, the medical image database 14, the user terminal device 16, and the image interpretation report database 18 via the network 22 in accordance with a predetermined protocol.

[0043] The medical image analysis device 20 may be a cloud server accessible from multiple medical institutions via the Internet. The processing performed by the medical image analysis device 20 may be a cloud service that is charged or flat-fee.

[0044] [Functional Configuration of Medical Image Analysis Apparatus] Figure 3 is a block diagram showing the functional configuration of the medical image analysis apparatus 20. Each function of the medical image analysis apparatus 20 is realized by the processor 20A executing a program stored in the memory 20B. As shown in Figure 3, the medical image analysis apparatus 20 includes a key image acquisition unit 32, a linking information extraction unit 34, a region of interest identification unit 42, and an output unit 48.

[0045] The key image acquisition unit 32 acquires a key image including a region of interest from the image interpretation report database 18 .

[0046] The linking information extraction unit 34 analyzes the key image and extracts linking information between the key image and the original medical image. In other words, the linking information is information for linking the key image with the original medical image. The linking information is, for example, information that appears in the key image separately from the subject. The linking information includes, for example, at least one of the series number, slice number, window width, window level, and annotation of the original medical image from which the key image was created. The linking information may be the result of alignment between the key image and the original medical image from which the key image was created. The linking information extraction unit 34 includes a character recognition unit 36, an image recognition unit 38, and an alignment result acquisition unit 40.

[0047] The character recognition unit 36 ​​analyzes characters in the key image by a known character recognition method such as OCR (Optical Character Recognition) to extract the linking information. The linking information extracted by the character recognition unit 36 ​​may include at least one of the window width, window level, slice number, and series number of the key image.

[0048] The image recognition unit 38 performs image recognition on the key image to extract linking information. The linking information extracted by the image recognition unit 38 may include at least one of the window width, window level, and annotation of the key image. The image recognition unit 38 includes an image recognition model 38A. The image recognition model 38A that extracts the window width or window level of the key image is a classification model or a regression model using a convolutional neural network (CNN). The image recognition model 38A that recognizes the annotation of the key image is a segmentation model or a detection model to which a convolutional neural network is applied. The image recognition unit 38 may include a plurality of image recognition models 38A from among a classification model, a regression model, a segmentation model, and a detection model. The image recognition model 38A is stored in the memory 20B.

[0049] The image recognition unit 38 also detects annotations added to the key image. The annotations detected by the image recognition unit 38 may include at least one of a circle, a rectangle, an arrow, a line segment, a point, and a scribble.

[0050] The registration result acquisition unit 40 acquires the result of registration between the key image and the medical image performed by the registration unit 44, which will be described later.

[0051] The region of interest identification unit 42 identifies a region of interest based on the linking information extracted by the linking information extraction unit 34. Using the linking information, the region of interest identification unit 42, for example, first estimates a position corresponding to the key image in the medical image from which the key image was created, and then identifies a region of interest in the medical image.

[0052] The region of interest identifying unit 42 may identify a region of interest from a two-dimensional image or from a three-dimensional image. The identified region of interest may be a two-dimensional region or a three-dimensional region.

[0053] The region of interest identification unit 42 includes a region of interest estimation model 42A, an alignment unit 44, and an annotation addition unit 46. The region of interest estimation model 42A is a deep learning model that, when an image is given as input, outputs the position of a region of interest within the input image. The region of interest estimation model 42A may be a trained model to which a CNN is applied. The region of interest estimation model 42A is stored in memory 20B.

[0054] The alignment unit 44 aligns the key image with the original medical image from which the key image was created. Aligning the key image with the original medical image from which the key image was created means matching the pixels of both images showing the same subject, such as an organ. The result of the alignment between the key image and the medical image by the alignment unit 44 includes the correspondence between the pixels of the key image and the pixels of the medical image. The annotation addition unit 46 adds annotations to the original medical image from which the key image was created.

[0055] The output unit 48 outputs the region of interest identified by the region of interest identification unit 42 and records it in a learning database (not shown). The region of interest to be output may be at least one of a mask, a bounding box, and a heat map that was added to the medical image from which the key image was created.

[0056] <Medical Image Analysis Method: First Embodiment> Figure 4 is a flowchart showing a medical image analysis method according to a first embodiment using a medical image analysis device 20. The medical image analysis method is a method for identifying a region of interest in a medical image from which a key image is created. The medical image analysis method is realized by the processor 20A executing a medical image analysis program stored in the memory 20B. The medical image analysis program may be provided by a computer-readable non-transitory storage medium or via the Internet.

[0057] In step S1, the key image acquisition unit 32 acquires a key image from the radiology report database 18. The key image acquisition unit 32 may acquire the key image from a source other than the radiology report database 18 via the network 22. The linking information extraction unit 34 performs image analysis on the acquired key image and extracts linking information required to link the key image to the medical image from which the key image was created. Image analysis includes character recognition and image recognition.

[0058] In the following step S2, the region of interest specifying unit 42 specifies the region of interest of the medical image from which the key image was created, based on the linking information extracted in step S1.

[0059] 5 is a diagram showing a key image and the medical image from which the key image was created. The key image IK1 shown in FIG. 5 is a two-dimensional image. The key image IK1 includes the character information "20220908," "SE: 2," "Compressed / Diagnostic Record Image," and "IM: 8." The character recognition unit 36 ​​recognizes these characters and extracts at least one of the window width, window level, slice number, and series number of the key image IK1 as linking information.

[0060] The key image IK1 also includes an arrow annotation AN1. The image recognition unit 38 performs image recognition on the key image IK1 and extracts the annotation AN1 as linked information. The image recognition unit 38 may also perform image recognition on the key image IK1 and extract at least one of a slice number, a series number, a window width, and a window level as linked information.

[0061] The medical image ID shown in FIG. 5 is a three-dimensional image from which the key image IK1 is created, and is an image in which a rectangular annotation AN2 is added to the region of interest identified by the region of interest identifying unit 42.

[0062] The enlarged image IZ shown in Figure 5 is an enlarged image of the area to which the medical image ID annotation AN2 is added. The coronal image IC shown in Figure 5 is an image of a coronal section including the area to which the medical image ID annotation AN2 is added. In this way, by identifying the region of interest in the three-dimensional medical image from which the key image was created, the region of interest in the medical image can be identified three-dimensionally. This allows for the creation of various types of images that include the region of interest, and medical images with identified regions of interest can be used as training data for a learning model that extracts regions of interest from images.

[0063] <Medical Image Analysis Method: Second Embodiment> FIG. 6 is a flowchart showing a medical image analysis method according to the second embodiment.

[0064] Step S11 is the same as step S1 in the first embodiment. Here, the image recognition unit 38 extracts the linking information from the key image using the image recognition model 38A. The character recognition unit 36 ​​extracts the linking information from the key image using OCR.

[0065] In step S12, if an annotation has been added to the key image acquired in step S11, the image recognition unit 38 detects the annotation from the key image.

[0066] In step S13, the region of interest identification unit 42 identifies a slice image of the original medical image that is at the same position as the key image, based on the slice number in the linking information extracted in step S11. If the slice number cannot be extracted in step S11, the region of interest identification unit 42 identifies a slice image at the same position as the key image using a known method.

[0067] In step S14, the registration unit 44 aligns the key image with the slice image identified in step S13. The key image may be cropped or rotated from the slice image of the original medical image, so registration may be necessary. Fig. 7 shows an example of a key image. The key image IK2 shown in Fig. 7 is a cropped key image without annotations.

[0068] In step S15, if an annotation has been added to the key image acquired in step S11, the annotation adding unit 46 adds the annotation to the slice image identified in step S13. By performing the alignment in step S14, the annotation adding unit 46 can add the annotation to the slice image at the same position as the annotation on the key image.

[0069] In step S16, the region-of-interest identifying unit 42 identifies a region of interest in the slice image based on the annotation added in step S15. Here, the region-of-interest identifying unit 42 identifies the region of interest using the region-of-interest estimation model 42A. The result of identifying the region of interest may be at least one of a mask, a bounding box, and a heat map. The output unit 48 outputs the identified region of interest.

[0070] In this way, by adding annotations of the key image to the slice image from which the key image was created and estimating the region of interest based on the annotations, it is possible to identify the region of interest in the slice image, and therefore the region of interest in the original medical image.

[0071] Here, we have described the case where annotations are added to the key image acquired in step S11, but the region of interest estimation model 42A can also estimate a region of interest from a key image that does not include annotations.

[0072] <Medical Image Analysis Method: Third Embodiment> FIG. 8 is a flowchart showing a medical image analysis method according to the third embodiment.

[0073] Step S21 is the same as step S11 in the second embodiment, and step S22 is the same as step S12 in the second embodiment.

[0074] In step S23, the region of interest identifying unit 42 identifies the region of interest in the key image acquired in step S21. Here, the region of interest identifying unit 42 identifies the region of interest using the region of interest estimation model 42A.

[0075] Step S24 is the same as step S13 in the second embodiment, and step S25 is the same as step S14 in the second embodiment.

[0076] In step S26, the region of interest specifying unit 42 adds the region of interest of the key image specified in step S23 to the slice image specified in step S24, and specifies the added region of interest as the region of interest of the slice image. By performing the alignment in step S25, the region of interest specifying unit 42 can add the region of interest to the slice image at the same position as the position of the region of interest in the key image.

[0077] As described above, the region of interest in the medical image may be identified by adding the region of interest identified in the key image to the medical image.

[0078] <Method of Estimating Corresponding Positions: Fourth Embodiment> The region of interest identification unit 42 estimates corresponding positions of key images in the medical images from which the key images were created based on the linking information. Here, a method of estimating corresponding positions will be described.

[0079] If the linking information extraction unit 34 can extract the series number as linking information from the key image, the region of interest identification unit 42 identifies the series of medical images from which the key image was created. If the series number cannot be extracted, the region of interest identification unit 42 searches all series to identify the series of medical images from which the key image was created.

[0080] The region of interest identifying unit 42 identifies the slice position of the original medical image when the linked information extracting unit 34 can extract the slice number from the key image. If the slice number cannot be extracted from the key image, the region of interest identifying unit 42 searches all slices to identify the slice position of the original medical image.

[0081] The region of interest identification unit 42 estimates the window level and the window width from the key image. The region of interest identification unit 42 may estimate the window level and the window width from the key image using a window level / window width estimation model (not shown) to which a CNN is applied.

[0082] The registration unit 44 normalizes the original image from which the key image was created using the window level and window width estimated by the region of interest identification unit 42. Finally, the registration unit 44 estimates corresponding positions using common non-rigid registration techniques, including rotation, translation, and scaling.

[0083] <Learning Method of Region-of-Interest Estimation Model: Fifth Embodiment> The region-of-interest identification unit estimates a region of interest using region-of-interest estimation model 42 A. Here, a learning method of region-of-interest estimation model 42 A will be described.

[0084] First, the user prepares a medical image in which the position of the region of interest is known, and creates a learning medical image from this medical image.

[0085] The training medical image may be, for example, an image obtained by cropping the area surrounding the region of interest of the medical image. The training medical image may also be an image in which a rectangle is added to the region of interest of the medical image. When creating a key image, a rectangle that is larger than the size of the region of interest is often added, so it is preferable to follow this size of the rectangle. The training medical image may also be an image in which an arrow is added to the region of interest of the medical image. The training medical image may also be a two-dimensional image like the key image.

[0086] From the created training medical image, a region of interest in the original medical image is estimated, i.e., a model that solves the inverse problem is learned, thereby creating a region of interest estimation model 42A.

[0087] That is, the region-of-interest estimation model 42A is obtained by machine learning using a training data set that is a set of training medical images and regions of interest of the images based on the training medical images. When a cropped image, an image with a rectangle attached, or an image with an arrow attached is given as input, the region-of-interest estimation model 42A outputs the region of interest of the input image.

[0088] The region of interest estimation model 42A thus trained can estimate a region of interest from the medical image from which the key image is created and from the key image.

[0089] According to the medical image analysis method, a key image is analyzed using image recognition technology, its position corresponding to the original medical image is estimated, and the region of interest intended by the doctor is identified by analyzing the analysis results and the original medical image.Therefore, the medical image with the identified region of interest can be used as training data for a learning model that estimates regions of interest from medical images.

[0090] The image analysis method according to this embodiment can be applied to images other than medical images. For example, the method can be applied to a technology for acquiring a diagnostic image of a region of interest created from an original image of social infrastructure facilities such as transportation, electricity, gas, and water, and identifying the region of interest in the original image from which the diagnostic image was created.

[0091] The technical scope of the present invention is not limited to the scope described in the above embodiments. The configurations and the like in each embodiment can be appropriately combined with each other within the scope that does not deviate from the spirit of the present invention.

[0092] DESCRIPTION OF SYMBOLS 10... Medical image analysis system 12... Medical image inspection equipment 14... Medical image database 16... User terminal device 16A... Input device 16B... Display 18... Radiography report database 20... Medical image analysis device 20A... Processor 20B... Memory 20C... Communication interface 22... Network 32... Key image acquisition unit 34... Information extraction unit 36... Character recognition unit 38... Image recognition unit 38A... Image recognition model 40... Result acquisition unit 42... Region of interest identification unit 42A... Region of interest estimation model 44... Alignment unit 46... Annotation addition unit 48... Output unit AN1... Annotation AN2... Annotation IC... Coronal image ID... Medical image IK1... Key image IK2... Key image IZ... Enlarged image S1-S2, S11-S16, S21-S26... Steps of medical image analysis method

Claims

1. A medical image analysis device comprising: at least one processor; and at least one memory for storing instructions to be executed by said at least one processor, wherein said at least one processor acquires a key image created from a medical image, said key image including a region of interest; analyzes said key image to extract linking information with the medical image from which said key image was created; and identifies said region of interest in said medical image based on said linking information.

2. The medical image analysis device according to claim 1, wherein the at least one processor estimates the region of interest from the key image and adds the estimated region of interest to the medical image.

3. The medical image analysis device of claim 1, wherein the key image includes an annotation indicating the region of interest, and the at least one processor adds the annotation to the medical image and identifies the region of interest in the medical image based on the added annotation.

4. The medical image analysis device according to claim 3, wherein the at least one processor detects annotations from the key image.

5. The medical image analysis device according to claim 1, wherein the medical images include at least one of a two-dimensional still image, a three-dimensional still image, and a moving image.

6. The medical image analysis device according to claim 1, wherein the key image is a result of volume rendering created from the medical image.

7. The medical image analysis device of claim 1, wherein the at least one processor analyzes characters in the key image by character recognition to extract the linking information, and the linking information includes at least one of a window width, a window level, a slice number, and a series number of the key image.

8. The medical image analysis device of claim 1, wherein the at least one processor performs image recognition on the key image to extract the linking information, and the linking information includes at least one of a window width, a window level, and an annotation of the key image.

9. The medical image analysis device according to claim 1, wherein the at least one processor extracts the linking information from the result of alignment between the medical image and the key image.

10. The medical image analysis device according to claim 1, wherein the at least one processor estimates the corresponding position of the key image in the medical image based on the linking information.

11. The medical image analysis device according to claim 1, wherein the region of interest is at least one of a mask, a bounding box, and a heat map.

12. The medical image analysis device according to any one of claims 1 to 11, wherein the medical image is a DICOM (Digital Imaging and Communications in Medicine) image.

13. A medical image analysis method comprising: obtaining a key image created from a medical image, the key image including a region of interest; analyzing the key image to extract linking information with the medical image from which the key image was created; and identifying the region of interest in the medical image based on the linking information.

14. A program for causing a computer to execute the medical image analysis method according to claim 13.

15. A non-transitory computer-readable recording medium on which the program according to claim 14 is recorded.

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