Image processing device, method and program

Abstract

To determine the presence / absence of an abnormality in a pancreas even when a portion of the pancreas is lost in an image processing device, a method, and a program.SOLUTION: A processor extracts a region of a pancreas from a medical image, extracts a plurality of sub regions for the extracted pancreas, determines the presence / absence of each of the plurality of sub regions, selects a discriminator used for determination of the presence / absence of an abnormality in the pancreas from the plural types of discriminators on the basis of the result of determination, and determines the presence / absence of the abnormality in the pancreas by using the selected discriminator.SELECTED DRAWING: Figure 3

Description

[Technical Field]

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

[0002] In recent years, advances in medical equipment such as CT (Computed Tomography) and MRI (Magnetic Resonance Imaging) devices have made it possible to perform image diagnosis using higher-quality, higher-resolution medical images. Furthermore, computer-aided diagnosis (CAD) has been put to practical use, which analyzes medical images to derive the probability of the presence of a lesion, its location, and other information, and presents this information to doctors, such as radiologists. For example, Patent Document 1 proposes a method for identifying a region of a target organ and extracting a region suspected of being abnormal based on diagnostic criteria established for that organ. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2009-219610 Summary of the Invention [Problem to be solved by the invention]

[0004] In diagnosing a target organ using CAD, it is important to identify changes in the organ's shape, such as atrophy or swelling. For example, if the target organ is the pancreas, a pancreatic tumor will cause the pancreatic parenchyma surrounding the tumor to swell, and the pancreatic parenchyma outside the tumor will atrophy. For this reason, it is important to determine the presence or absence of pancreatic disease by focusing on the diameter of the pancreas contained in medical images.

[0005] However, in cases where atrophy has progressed to an extreme degree, the pancreas may become so thin that part of the pancreas may disappear from the medical image, making it impossible to determine whether or not there is an abnormality.

[0006] The present invention has been made in view of the above circumstances, and aims to make it possible to determine the presence or absence of an abnormality in the pancreas even when part of the pancreas has disappeared. [Means for solving the problem]

[0007] An image processing device according to a first aspect of the present disclosure includes at least one processor, The processor extracts a region of the pancreas from the medical image; Extracting a plurality of sub-regions from the extracted pancreas; determining the presence or absence of each of a plurality of sub-regions; Based on the result of the determination, a classifier to be used for determining the presence or absence of an abnormality in the pancreas is selected from the plurality of types of classifiers; The selected classifier is used to determine whether or not there is an abnormality in the pancreas.

[0008] An image processing device according to a second aspect of the present disclosure may be an image processing device according to the first aspect, wherein the plurality of classifiers include a classifier that determines the presence or absence of an abnormality depending on a combination of the presence or absence of a plurality of sub-regions.

[0009] An image processing device according to a third aspect of the present disclosure may be an image processing device according to the second aspect, in which the classifier is trained using images consisting of a combination of multiple sub-regions with and without presence as training data.

[0010] An image processing device according to a fourth aspect of the present disclosure may be an image processing device according to the first aspect, wherein the plurality of classifiers include a classifier that determines whether or not an abnormality exists based on sub-areas that are determined to have an abnormality.

[0011] An image processing device according to a fifth aspect of the present disclosure is an image processing device according to any one of the second to fourth aspects, wherein the plurality of classifiers may determine the presence or absence of an abnormality based on the characteristics of the subregions.

[0012] An image processing device according to a sixth aspect of the present disclosure is the image processing device according to the fifth aspect, wherein the feature of the sub-region may be at least one of diameter, size, and texture of the sub-region.

[0013] An image processing device according to a seventh aspect of the present disclosure is an image processing device according to any one of the first to sixth aspects, wherein the plurality of classifiers may include a classifier that determines that there is an abnormality in the pancreas when at least one of the plurality of sub-regions is absent.

[0014] An image processing device according to an eighth aspect of the present disclosure is the image processing device according to any one of the first to seventh aspects, wherein the plurality of sub-regions may be the head, body, and tail of the pancreas.

[0015] The image processing method according to the present disclosure includes extracting a pancreas region from a medical image; Extracting a plurality of sub-regions from the extracted pancreas; determining the presence or absence of each of a plurality of sub-regions; Based on the result of the determination, a classifier to be used for determining the presence or absence of an abnormality in the pancreas is selected from the plurality of types of classifiers; The selected classifier is used to determine whether or not there is an abnormality in the pancreas.

[0016] The image processing program according to the present disclosure includes a procedure for extracting a pancreas region from a medical image, extracting a plurality of sub-regions from the extracted pancreas; determining the presence or absence of each of a plurality of sub-regions; a step of selecting a classifier to be used for determining the presence or absence of pancreatic abnormality from among a plurality of types of classifiers based on the result of the determination; The computer is caused to execute a procedure for determining the presence or absence of an abnormality in the pancreas using the selected classifier. [Effects of the Invention]

[0017] According to the present disclosure, it is possible to determine whether or not there is an abnormality in the pancreas even when part of the pancreas has disappeared. [Brief explanation of the drawings]

[0018] [Figure 1] FIG. 1 is a diagram illustrating a schematic configuration of a diagnosis support system to which an image processing device according to an embodiment of the present disclosure is applied; [Figure 2] FIG. 1 is a diagram showing the hardware configuration of an image processing apparatus according to an embodiment of the present invention; [Figure 3] Functional configuration diagram of an image processing apparatus according to this embodiment [Figure 4] Diagram for explaining extraction of the pancreas region [Figure 5] Diagram illustrating division of the pancreas into head, body, and tail [Figure 6] A medical image showing a pancreas with a missing tail. [Figure 7] FIG. 1 shows a classifier used in this embodiment. [Figure 8] FIG. 10 is a diagram showing a display screen for determining whether or not an abnormality has occurred. [Figure 9] A flowchart showing the processing performed in this embodiment [Figure 10] FIG. 10 is a diagram showing another example of extracting a subregion. DETAILED DESCRIPTION OF THE INVENTION

[0019] Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings. First, the configuration of a medical information system to which an image processing device according to this embodiment is applied will be described. FIG. 1 is a diagram showing a schematic configuration of the medical information system. In the medical information system shown in FIG. 1, a computer 1 incorporating an image processing device according to this embodiment, an imaging device 2, and an image storage server 3 are connected in a communicable state via a network 4.

[0020] The computer 1 includes an image processing device according to this embodiment and has the image processing program of this embodiment installed. The computer 1 may be a workstation or personal computer operated directly by a doctor making a diagnosis, or may be a server computer connected to either of these via a network. The image processing program is stored in a storage device of the server computer connected to the network or in network storage in an externally accessible state, and is downloaded and installed into the computer 1 used by the doctor upon request. Alternatively, the program may be recorded on a recording medium such as a DVD (Digital Versatile Disc) or CD-ROM (Compact Disc Read Only Memory) and distributed, and then installed into the computer 1 from the recording medium.

[0021] The imaging device 2 is a device that captures an image of a region of a subject to be diagnosed and generates a three-dimensional image representing the region, and specifically includes a CT device, an MRI device, a PET (Positron Emission Tomography) device, etc. The three-dimensional image composed of multiple tomographic images generated by the imaging device 2 is transmitted to and stored in the image storage server 3. In this embodiment, the imaging device 2 is a CT device, and generates a three-dimensional CT image of the subject's abdomen. The acquired CT image may be a contrast-enhanced CT image or a non-contrast-enhanced CT image.

[0022] The image storage server 3 is a computer that stores and manages various data, and is equipped with a large-capacity external storage device and database management software. The image storage server 3 communicates with other devices via a wired or wireless network 4, sending and receiving image data and the like. Specifically, it acquires various data, including image data of CT images generated by the imaging device 2, via the network, and stores and manages them on recording media such as a large-capacity external storage device. The storage format of the image data and communication between devices via the network 4 are based on protocols such as DICOM (Digital Imaging and Communication in Medicine).

[0023] Next, an image processing device according to this embodiment will be described. Fig. 2 is a diagram showing the hardware configuration of the image processing device according to this embodiment. As shown in Fig. 2, the image processing device 20 includes a CPU (Central Processing Unit) 11, non-volatile storage 13, and memory 16 as a temporary storage area. The image processing device 20 also includes a display 14 such as a liquid crystal display, input devices 15 such as a keyboard and a mouse, and a network I / F (Interface) 17 connected to a network 4. The CPU 11, storage 13, display 14, input devices 15, memory 16, and network I / F 17 are connected to a bus 18. The CPU 11 is an example of a processor in the present disclosure.

[0024] The storage 13 is realized by a hard disk drive (HDD), a solid state drive (SSD), a flash memory, etc. The storage 13 as a storage medium stores an image processing program 12. The CPU 11 reads the image processing program 12 from the storage 13, loads it into the memory 16, and executes the loaded image processing program 12.

[0025] Next, the functional configuration of the image processing device according to this embodiment will be described. Fig. 3 is a diagram showing the functional configuration of the image processing device according to this embodiment. As shown in Fig. 3, the image processing device 20 includes an image acquisition unit 21, a first extraction unit 22, a second extraction unit 23, a first determination unit 24, a selection unit 25, a second determination unit 26, and a display control unit 27. When the CPU 11 executes the image processing program 12, the CPU 11 functions as the image acquisition unit 21, the first extraction unit 22, the second extraction unit 23, the first determination unit 24, the selection unit 25, the second determination unit 26, and the display control unit 27.

[0026] The image acquisition unit 21 acquires a medical image G0 to be processed from the image storage server 3 in response to an instruction from an operator via the input device 15. In this embodiment, the medical image G0 is a CT image consisting of multiple tomographic images including the abdomen of a human body.

[0027] The first extraction unit 22 extracts a pancreas region from the medical image G0. To this end, the first extraction unit 22 has a semantic segmentation model (hereinafter referred to as an SS (Semantic Segmentation) model) that has been machine-learned to extract the pancreas from the medical image G0. As is well known, the SS model is a machine learning model that outputs an output image in which a label representing an extracted object (class) is assigned to each pixel of an input image. In this embodiment, the input image is a tomographic image that constitutes the medical image G0, the extracted object is the pancreas, and the output image is an image in which the pancreas region is labeled. The SS model is constructed using a convolutional neural network (CNN) such as Residual Networks (ResNet) or U-shaped Networks (U-Net).

[0028] As a result, the first extraction unit 22 extracts the region of the pancreas 30 included in the medical image G0 as shown in Fig. 4. Note that Fig. 4 shows one tomographic image D0 included in the medical image G0.

[0029] The extraction of the pancreas is not limited to using the SS model, and any method for extracting the pancreas from the medical image G0, such as template matching or threshold processing of the CT value, can be applied.

[0030] The second extraction unit 23 extracts a plurality of sub-regions from the pancreas extracted by the first extraction unit 22. In this embodiment, the second extraction unit 23 divides the region of the pancreas into a head, a body, and a tail, and extracts each of the head, body, and tail from the region of the pancreas as a sub-region.

[0031] FIG. 5 is a diagram illustrating the division of the pancreas into the head, body, and tail. FIG. 5 shows the pancreas as viewed from the front of the human body. In the following description, up, down, left, and right refer to a front view of a human body in an upright position. As shown in FIG. 5, when viewed from the front, a vein 31 and an artery 32 run parallel to each other in the vertical direction behind the pancreas 30. Anatomically, the pancreas 30 is divided into the head on the left side of the vein 31, the body between the vein 31 and the artery 32, and the tail on the right side of the artery 32. Therefore, in this embodiment, the second extraction unit 23 divides the pancreas 30 into three small regions: the head 33, the body 34, and the tail 35, based on the vein 31 and the artery 32. The boundaries of the head 33, the body 34, and the tail 35 are based on the boundary definitions described in "Pancreatic Cancer Treatment Guidelines, 7th Edition, Supplementary Edition, Edited by the Japan Pancreas Society, p. 12, September 2020." Specifically, the left edge of the vein 31 (the right edge of the vein 31 when viewed from the front) is the boundary between the head 33 and the body 34, and the left edge of the artery 32 (the right edge of the artery 32 when viewed from the front) is the boundary between the body 34 and the tail 35.

[0032] For segmentation, the second extraction unit 23 extracts veins 31 and arteries 32 near the pancreas 30 in the medical image G0. The second extraction unit 23 extracts vascular regions and their centerlines (i.e., central axes) from the region near the pancreas 30 in the medical image G0, for example, using the methods described in Japanese Patent Application Laid-Open Nos. 2010-200925 and 2010-220732. This method first calculates the positions and major axis directions of multiple candidate points constituting the vascular centerlines based on the values ​​of voxel data constituting the medical image G0. Alternatively, the second extraction unit 23 calculates a Hessian matrix for the medical image G0 and analyzes the eigenvalues ​​of the calculated Hessian matrix to calculate the position information and major axis directions of multiple candidate points constituting the vascular centerlines. Then, the second extraction unit 23 calculates feature values ​​representing the likelihood of blood vessels for the voxel data around the candidate points, and determines whether the voxel data represents a blood vessel based on the calculated feature values. As a result, blood vessel regions and their core lines are extracted from the medical image G0. The second extraction unit 23 divides the pancreas 30 into a head 33, a body 34, and a tail 35 based on the left edge (the right edge when the human body is viewed from the front) of the extracted vein 31 and artery 32, and extracts each of the head 33, body 34, and tail 35 as a sub-region.

[0033] When a tumor develops in the pancreas, the pancreatic parenchyma around the tumor swells, and the pancreatic parenchyma outside the tumor shrinks. If the atrophy of the pancreatic parenchyma progresses to an extreme extent, the pancreas 30 may become so thin that it cannot be seen in the medical image G0, resulting in a partial loss of the pancreas in the medical image G0. Furthermore, a portion of the pancreas 30 may be missing due to a congenital malformation. In such cases, it is not possible to extract all of the subregions of the head 33, body 34, and tail 35 from the medical image G0. Therefore, the second extraction unit 23 extracts only the subregions that can be extracted.

[0034] The extraction of the head 33, body 34, and tail 35 as sub-regions from the pancreas 30 is not limited to the above method. For example, the pancreas 30 may be divided into the head 33, body 34, and tail 35 and the head 33, body 34, and tail 35 may be extracted as sub-regions by using a segmentation model that has been machine-learned to extract the head 33, body 34, and tail 35 from the pancreas 30. In this case, the segmentation model may be trained by preparing multiple sets of training data each consisting of a training image including the pancreas and a mask image obtained by dividing the pancreas into the head, body, and tail based on the boundary definition described above.

[0035] The first determination unit 24 determines the presence or absence of each of the multiple subregions. Specifically, the first determination unit 24 determines the presence or absence of the head 33, body 34, and tail 35. Here, a normal pancreas includes all of the subregions, namely, the head 33, body 34, and tail 35, as shown in FIGS. 4 and 5. On the other hand, as described above, if the atrophy of the pancreatic parenchyma progresses to an extreme extent, the pancreas becomes so thin that it cannot be seen in the medical image G0, and the pancreatic parenchyma disappears from the medical image G0. For example, if the tail 35 of the pancreas 30 is extremely atrophied, the tail 35 of the pancreas 30 disappears from the medical image G0, as shown in FIG. 6. Note that the missing tail 35 is indicated by a dashed line in FIG. 6. In this case, the tail 35 will not be extracted by the second extraction unit 23. Furthermore, if the tail 35 is missing due to a congenital malformation, the tail 35 will not be extracted by the second extraction unit 23.

[0036] In this way, when the tail 35 is not extracted by the second extraction unit 23, the first determination unit 24 determines that the head 33 is present, the body 34 is present, and the tail 35 is absent.

[0037] The selection unit 25 selects a classifier to be used for determining the presence or absence of pancreatic abnormality from among a plurality of types of classifiers based on the determination result by the first determination unit 24. The classifier in this embodiment is a classifier for determining the presence or absence of pancreatic abnormality. In this embodiment, a plurality of classifiers are prepared according to combinations of the presence or absence of sub-regions.

[0038] In this embodiment, a first classifier 41, a second classifier 42, and a third classifier 43 are prepared as shown in Fig. 7. The first classifier 41 is a classifier for pancreases from which the head has not been extracted, the second classifier 42 is a classifier for pancreases from which the body has not been extracted, and the third classifier 43 is a classifier for pancreases from which the tail has not been extracted.

[0039] As described above, when the tail 35 is missing, the first determination unit 24 determines that the head 33 is present, the body 34 is present, and the tail 35 is absent. Therefore, the selection unit 25 selects the third classifier 43 from the first to third classifiers 41 to 43.

[0040] Here, the first to third classifiers 41 to 43 are made up of convolutional neural networks that have undergone machine learning using medical images consisting of a combination of the presence and absence of multiple sub-regions as training data, so as to output an evaluation value representing the presence or absence of pancreatic abnormality based on the characteristics of each sub-region. The evaluation value representing the presence or absence of pancreatic abnormality output by the first to third classifiers 41 to 43 is the probability of representing pancreatic abnormality and is a value between 0 and 1.

[0041] Here, the first classifier 41 for the pancreas, where the head has not been extracted, is constructed by training a neural network using multiple training data in which the features of the head, body, and tail of the pancreas extracted from a medical image in which the head of the pancreas has disappeared are used as training data, and the presence or absence of abnormalities is used as correct answer data.

[0042] The second classifier 42 for the pancreas, from which the body has not been extracted, is constructed by training a neural network using multiple training data in which the features of the head, body, and tail of the pancreas extracted from a medical image in which the body of the pancreas has disappeared are used as training data, and the presence or absence of abnormalities is used as correct answer data.

[0043] The third classifier 43 for the pancreas from which the tail has not been extracted is constructed by training a neural network using multiple training data in which the features of the head, body, and tail of the pancreas extracted from a medical image in which the tail of the pancreas has disappeared are used as training data, and the presence or absence of abnormalities is used as correct answer data.

[0044] The characteristics of the subregion include at least one of the diameter, size, texture, etc. of the subregion. The diameter of the subregion can be the diameter of a cross section intersecting the long axis of the pancreas. Note that the diameter of the pancreas varies at each position along the long axis of the pancreas. Therefore, multiple cross sections intersecting the long axis of the pancreas at predetermined intervals along the long axis of the pancreas can be set, and a representative value of the diameters in the multiple cross sections (e.g., maximum, minimum, median, average, etc.) can be used as the diameter of the subregion. Furthermore, because the cross sections intersecting the long axis of the pancreas are not circular, a representative value of the diameters in multiple directions intersecting the long axis of the pancreas (e.g., maximum, minimum, median, average, etc.) can be used as the diameter of the subregion. The size of the subregion can be calculated from the number of voxels in the subregion in the medical image G0 and the spacing between voxels. The texture of the subregion is the pixel value of each pixel in the subregion in the medical image G0 (CT value in the case of a CT image).

[0045] In the medical image G0, there are two situations in which a subregion of the pancreas has disappeared: (1) a subregion has disappeared due to atrophy as a result of the progression of a pancreatic disease (pathological disappearance); and (2) a subregion has disappeared due to a congenital malformation (i.e., it was not originally present) (congenital disappearance). Considering these two cases, two patterns are possible: either only pathological disappearance is considered to be abnormal, or both pathological disappearance and congenital disappearance are considered to be abnormal. For this reason, when training the classifiers 41 to 43, it is necessary to determine which of the two patterns is considered to be abnormal and train the neural network accordingly.

[0046] When training is performed using training data that considers only pathological loss to be abnormal, the first to third classifiers 41 to 43 determine whether a missing sub-region is pathological or congenital based on the characteristics of the multiple sub-regions, and determine whether or not there is an abnormality. On the other hand, when training is performed using training data that considers both pathological loss and congenital loss to be abnormal, the first to third classifiers 41 to 43 output a determination result that a sub-region has been lost and is therefore abnormal, based on the characteristics of the multiple input sub-regions, i.e., an evaluation value close to 1.

[0047] The first to third classifiers 41 to 43 may determine an abnormal region in the pancreas in addition to the evaluation result of the presence or absence of an abnormality. The abnormal region determination result is an evaluation value representing the probability that each pixel in the medical image G0 is an abnormal region, and this is also a value between 0 and 1.

[0048] Furthermore, the first to third classifiers 41 to 43 are not limited to those that determine the presence or absence of an abnormality in the pancreas based on the features of the sub-regions. The first to third classifiers 41 to 43 may be configured to extract the features of each sub-region from the medical image G0 when the medical image G0 is input and determine the presence or absence of an abnormality in the pancreas. Furthermore, the first to third classifiers 41 to 43 may be configured to determine that an abnormality exists in the pancreas when at least one of the sub-regions is absent.

[0049] On the other hand, there may be cases where the determination result by the first determination unit 24 indicates that all sub-regions are present. For this reason, a fourth classifier for the pancreas from which all sub-regions have been extracted may be separately prepared, and when the determination result by the first determination unit 24 indicates that the head, body, and tail are present, the selection unit 25 may select the fourth classifier.

[0050] The second determination unit 26 determines the presence or absence of an abnormality in the pancreas using the classifier selected by the selection unit 25. To this end, the second determination unit 26 derives features of each sub-region from the medical image G0, and inputs the derived features to the selected classifier to determine the presence or absence of an abnormality in the pancreas.

[0051] Then, second determination unit 26 compares the evaluation value output by the selected classifier with a predetermined threshold, and outputs a determination result that there is an abnormality if the evaluation value is equal to or greater than the threshold. Also, if classifiers 41 to 43 are used to extract abnormal regions in the pancreas, they determine as abnormal regions any region for which the evaluation value indicating that it is an abnormal region is equal to or greater than the threshold.

[0052] The display control unit 27 displays the determination result of the presence or absence of an abnormality on the display 14. FIG. 8 is a diagram showing a display screen of the determination result. As shown in FIG. 8, a medical image G0 in the case where it has been determined that an abnormality is present is displayed on a display screen 50. The displayed medical image G0 is, for example, a tomographic image including an area in which an abnormality is found. In FIG. 8, an abnormal area 51 determined in the medical image G0 is hatched. A determination result 52 of the presence or absence of an abnormality is also displayed. In FIG. 8, in addition to "abnormality present," the value of 0.9, which is the evaluation value output by the selected classifier, is also displayed.

[0053] Next, the processing performed in this embodiment will be described. Fig. 9 is a flowchart showing the processing performed in this embodiment. First, the image acquisition unit 21 acquires a medical image G0 from the storage 13 (step ST1), and the first extraction unit 22 extracts a pancreas region from the medical image G0 (step ST2). Next, the second extraction unit 23 extracts a plurality of sub-regions from the pancreas extracted by the first extraction unit 22 (step ST3). Then, the first determination unit 24 determines the presence or absence of each of the plurality of sub-regions (step ST4).

[0054] Next, the selection unit 25 selects a classifier to be used for determining the presence or absence of pancreatic abnormality from the plurality of types of classifiers based on the determination result by the first determination unit 24 (step ST5). Next, the second determination unit 26 determines the presence or absence of pancreatic abnormality using the classifier selected by the selection unit 25 (step ST6). Then, the display control unit 27 displays the determination result of the presence or absence of abnormality (step ST7), and the process ends.

[0055] In this manner, in this embodiment, the presence or absence of an abnormality in the pancreas is determined using a classifier selected depending on the presence or absence of a sub-region. Therefore, even if part of the pancreas is missing in the medical image G0, the presence or absence of an abnormality in the pancreas can be determined.

[0056] In the above embodiment, a head classifier, a body classifier, and a tail classifier may be provided instead of the first classifier 41, the second classifier 42, and the third classifier 43, and the selection unit 25 may select one of the head classifier, the body classifier, and the tail classifier depending on whether a sub-region is present. In this case, if the first determination unit 24 determines that a head 33 is present, a body 34 is present, and a tail 35 is absent, the selection unit 25 selects the head classifier and the body classifier. The second determination unit 26 uses the head classifier and the body classifier selected by the selection unit 25 to determine the presence or absence of an abnormality in the pancreas using the features of the head and body of the pancreas region extracted from the medical image G0.

[0057] Here, the head classifier, body classifier, and tail classifier are constructed by machine learning to determine the presence or absence of pancreatic abnormalities from features of only the head, body, and tail of the pancreas, respectively.

[0058] In the above embodiment, the sub-regions extracted from the pancreas are the head, body, and tail, but this is not limiting. For example, as shown in Figure 10, the pancreas may be divided at equal intervals along the longitudinal axis 36 of the pancreas to extract sub-regions.

[0059] Furthermore, in the above embodiment, the processing for the case where only one of the head, body, and tail of the pancreas is missing has been described, but the present invention is not limited to this. A classifier for the case where any two of the head, body, and tail of the pancreas are missing may be prepared, and when any two of the head, body, and tail of the pancreas are missing, this classifier may be selected to determine whether or not there is an abnormality in the pancreas.

[0060] In the above embodiment, a CT image is used as the medical image G0, but this is not limiting. Any image, such as a three-dimensional image such as an MRI image, or a radiological image obtained by simple imaging, can be used as the medical image G0.

[0061] Furthermore, in the above embodiment, the following various processors can be used as the hardware structure of the processing units that perform various processes, such as the image acquisition unit 21, the first extraction unit 22, the second extraction unit 23, the first determination unit 24, the selection unit 25, the second determination unit 26, and the display control unit 27. As described above, the various processors include a CPU, which is a general-purpose processor that executes software (programs) and functions as various processing units, as well as dedicated electrical circuits that are processors having a circuit configuration specifically designed to perform specific processes, such as a programmable logic device (PLD), which is a processor whose circuit configuration can be changed after manufacture, such as an FPGA (Field Programmable Gate Array), and an ASIC (Application Specific Integrated Circuit).

[0062] A single processing unit may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs or a combination of a CPU and an FPGA). Also, multiple processing units may be configured with a single processor.

[0063] Examples of configuring multiple processing units with a single processor include, first, a form in which one processor is configured with a combination of one or more CPUs and software, and this processor functions as multiple processing units, as typified by computers such as client and server. Second, a form in which a processor is used to realize the functions of an entire system including multiple processing units with a single IC (Integrated Circuit) chip, as typified by systems on chips (SoCs). In this way, various processing units are configured using one or more of the above-mentioned various processors as a hardware structure.

[0064] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. [Explanation of symbols]

[0065] 1. Computer 2. Imaging equipment 3. Image storage server 4 Network 11 CPU 12 Image Processing Programs 13. Storage 14 Display 15 Input Devices 16 memory 17 Network I / F 18 Bus 20 Image processing device 21 Image acquisition unit 22 1st extraction part 23 Second extraction part 24 1st Judgment Section 25 Selection section 26 Second Judgment Section 27 Display control unit 30 Pancreas 31 Veins 32 arteries 33 Head 34 body part 35 Tail 36 Long axis 41 First classifier 42 Second classifier 43 Third Classifier 50 display screen 51 Abnormal area 52 Judgment result D0 Tomographic image G0 Medical Imaging

Claims

1. at least one processor; The processor: Extract the pancreas region from the medical image, Extracting a plurality of sub-regions from the extracted pancreas; determining the presence or absence of each of the plurality of sub-regions; selecting a classifier to be used for determining the presence or absence of an abnormality in the pancreas from a plurality of types of classifiers based on the result of the determination; An image processing device that determines the presence or absence of an abnormality in the pancreas using the selected classifier.

2. The image processing apparatus according to claim 1 , wherein the plurality of types of classifiers include a classifier that determines the presence or absence of the abnormality depending on a combination of the presence or absence of the plurality of sub-regions.

3. The image processing apparatus according to claim 2 , wherein the classifier is trained using images each consisting of a combination of the presence and absence of the plurality of sub-regions as training data.

4. The image processing apparatus according to claim 1 , wherein the plurality of types of classifiers include a classifier that determines the presence or absence of the abnormality based on the sub-regions determined to have an abnormality.

5. The image processing apparatus according to claim 2 , wherein the plurality of types of classifiers determine the presence or absence of an abnormality based on features of the sub-regions determined to have an abnormality.

6. The image processing device according to claim 5 , wherein the feature of the sub-region determined to be present is at least one of the diameter, size, and texture of the sub-region.

7. The image processing device according to claim 1 , wherein the plurality of types of classifiers include a classifier that determines that there is an abnormality in the pancreas when at least one of the plurality of sub-regions is absent.

8. The image processing device according to claim 1 , wherein the plurality of sub-regions are the head, body, and tail of the pancreas.

9. A computer extracts a region of the pancreas from a medical image, Extracting a plurality of sub-regions from the extracted pancreas; determining the presence or absence of each of the plurality of sub-regions; selecting a classifier to be used for determining the presence or absence of an abnormality in the pancreas from a plurality of types of classifiers based on the result of the determination; An image processing method for determining the presence or absence of an abnormality in the pancreas using the selected classifier.

10. A procedure for extracting a pancreas region from a medical image; extracting a plurality of sub-regions from the extracted pancreas; determining the presence or absence of each of the plurality of sub-regions; a step of selecting a classifier to be used for determining the presence or absence of an abnormality in the pancreas from a plurality of types of classifiers based on the result of the determination; and a procedure for determining the presence or absence of an abnormality in the pancreas using the selected classifier.

Images