WO2022270152A1 - 画像処理装置、方法およびプログラム - Google Patents

画像処理装置、方法およびプログラム Download PDF

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Publication number
WO2022270152A1
WO2022270152A1 PCT/JP2022/018960 JP2022018960W WO2022270152A1 WO 2022270152 A1 WO2022270152 A1 WO 2022270152A1 JP 2022018960 W JP2022018960 W JP 2022018960W WO 2022270152 A1 WO2022270152 A1 WO 2022270152A1
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Prior art keywords
evaluation value
target organ
evaluation
image processing
pancreas
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English (en)
French (fr)
Japanese (ja)
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彩 小笠原
瑞希 武井
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Fujifilm Corp
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Fujifilm Corp
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Priority to JP2023529662A priority Critical patent/JPWO2022270152A1/ja
Publication of WO2022270152A1 publication Critical patent/WO2022270152A1/ja
Priority to US18/528,754 priority patent/US20240112786A1/en
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30092—Stomach; Gastric

Definitions

  • the present disclosure relates to an image processing device, method and program.
  • CT Computer-aided diagnosis
  • MRI Magnetic Resonance Imaging
  • the lesion may not be clearly depicted in the medical image.
  • pancreatic cancer-related tumors are relatively clearly visualized, but in non-contrast-enhanced tomographic images, almost no pancreatic cancer-related tumors are visualized.
  • Conventional CAD is developed on the premise that lesions are clearly drawn on medical images to some extent, so it has been difficult to find lesions that are hardly drawn as described above.
  • the method described in International Publication No. 2010-035517 can only determine the presence or absence of lesions in a partial region of the target organ.
  • the shape change of the target organ is not localized in a small region, the shape change of the small region can be accurately detected by simply integrating the evaluation values of the small region as in the method described in Liu et al. I cannot judge.
  • the present disclosure has been made in view of the above circumstances, and aims to enable an abnormality of a target organ to be evaluated with high accuracy in consideration of the relationship between multiple small regions in the target organ.
  • An image processing apparatus comprises at least one processor, a processor extracting a target organ from the medical image; Set multiple small regions in the target organ, Deriving a first evaluation value representing the physical quantity of each of the plurality of small regions, deriving at least one second evaluation value representing the relationship of the first evaluation values between the plurality of small regions; Based on the second evaluation value, a third evaluation value that suggests the presence or absence of abnormality in the entire target organ is derived.
  • the first evaluation value is a physical quantity related to the size of the small area
  • the second evaluation value may be an evaluation value relating to the difference in size between small regions.
  • the processor sets an axis passing through the target organ, A small area may be set along the axis.
  • the processor may display the evaluation result based on the third evaluation value on the display.
  • the evaluation result based on the third evaluation value may be the probability of occurrence of a finding representing the feature of the shape of the target organ.
  • the findings may include at least one of atrophy, swelling, stenosis, and dilation occurring in the target organ.
  • the processor distinguishes the position of a small region having a relatively high degree of contribution to the evaluation result in the target organ from the position of a small region having a relatively low degree of contribution, and displays the position on the display. can be anything.
  • the processor displays on the display at least one of the first evaluation value and the second evaluation value of the small region having a relatively high degree of contribution to the evaluation result in the target organ.
  • the medical image is a tomographic image of the abdomen including the pancreas
  • the target organ may be the pancreas.
  • the processor may set small regions by dividing the pancreas into the head, body and tail.
  • An image processing method extracts a target organ from a medical image, Set multiple small regions in the target organ, Deriving a first evaluation value representing the physical quantity of each of the plurality of small regions, deriving at least one second evaluation value representing the relationship of the first evaluation values between the plurality of small regions; Based on the second evaluation value, a third evaluation value that suggests the presence or absence of abnormality in the entire target organ is derived.
  • the image processing method according to the present disclosure may be provided as a program for causing a computer to execute it.
  • the abnormality of the target organ can be evaluated with high accuracy.
  • a diagram for explaining the derivation of the diameter in the cross section of the pancreas A diagram showing a schematic configuration of a recurrent neural network
  • Flowchart showing processing performed in the present embodiment Diagram to illustrate the main pancreatic duct and pancreatic parenchyma A diagram schematically showing a cross section of the pancreas
  • FIG. 1 is a diagram showing a schematic configuration of a medical information system.
  • a computer 1 including an image processing apparatus according to this embodiment, an imaging apparatus 2, and an image storage server 3 are connected via a network 4 in a communicable state.
  • the computer 1 contains the image processing apparatus according to this embodiment, and the image processing program according to this embodiment is installed.
  • the computer 1 may be a workstation or personal computer directly operated by a doctor who diagnoses, or a server computer connected to them via a network.
  • the image processing program is stored in a storage device of a server computer connected to a network or in a network storage in an externally accessible state, and is downloaded and installed on the computer 1 used by a doctor upon request. Alternatively, it is recorded on a recording medium such as a DVD (Digital Versatile Disc) or a CD-ROM (Compact Disc Read Only Memory), distributed, and installed in the computer 1 from the recording medium.
  • a recording medium such as a DVD (Digital Versatile Disc) or a CD-ROM (Compact Disc Read Only Memory), distributed, and installed in the computer 1 from the recording medium.
  • the imaging device 2 is a device that generates a three-dimensional image representing the site by imaging the site to be diagnosed of the subject. ) equipment, etc.
  • a three-dimensional image composed of a plurality of tomographic images generated by the imaging device 2 is transmitted to the image storage server 3 and stored.
  • the imaging device 2 is a CT device, and generates a CT image of the chest and abdomen of the subject as a three-dimensional image.
  • the acquired CT image may be a contrast-enhanced CT image or a non-contrast CT image.
  • 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 to transmit and receive image data and the like.
  • various data including image data of a three-dimensional image generated by the photographing device 2 are acquired via a network, stored in a recording medium such as a large-capacity external storage device, and managed.
  • the image data storage format and communication between devices via the network 4 are based on protocols such as DICOM (Digital Imaging and Communication in Medicine).
  • FIG. 2 is a diagram showing the hardware configuration of the image processing apparatus according to this embodiment.
  • the image processing device 20 includes a CPU (Central Processing Unit) 11, a nonvolatile storage 13, and a memory 16 as a temporary storage area.
  • the image processing apparatus 20 also includes a display 14 such as a liquid crystal display, an input device 15 such as a keyboard and a mouse, and a network I/F (InterFace) 17 connected to the network 4 .
  • CPU 11 , storage 13 , display 14 , input device 15 , memory 16 and network I/F 17 are connected to bus 18 .
  • the CPU 11 is an example of a processor in the present disclosure.
  • the storage 13 is realized by HDD (Hard Disk Drive), SSD (Solid State Drive), flash memory, and the like.
  • the image processing program 12 is stored in the storage 13 as a storage medium.
  • the CPU 11 reads out the image processing program 12 from the storage 13 , expands it in the memory 16 , and executes the expanded image processing program 12 .
  • FIG. 3 is a diagram showing the functional configuration of the image processing apparatus according to this embodiment.
  • the image processing apparatus 20 includes an image acquisition unit 21, a target organ extraction unit 22, a small region setting unit 23, a first evaluation value derivation unit 24, a second evaluation value derivation unit 25, a third An evaluation value derivation unit 26 and a display control unit 27 are provided.
  • the CPU 11 includes an image acquisition unit 21, a target organ extraction unit 22, a small area setting unit 23, a first evaluation value derivation unit 24, and a second evaluation value derivation unit. 25 , functions as a third evaluation value derivation unit 26 and a display control unit 27 .
  • the image acquisition unit 21 acquires the target image G0 to be processed from the image storage server 3 according to an instruction from the input device 15 by the operator.
  • the target image G0 is a CT image composed of a plurality of tomographic images including the chest and abdomen of the human body, as described above.
  • the target image G0 is an example of the medical image of the present disclosure.
  • the target organ extraction unit 22 extracts target organs from the target image G0.
  • the target organ is the pancreas.
  • the target organ extraction unit 22 consists of a semantic segmentation model (hereinafter referred to as an SS (Semantic Segmentation) model) that has undergone machine learning so as to extract the pancreas from the target image G0.
  • the SS model is a machine learning model that outputs an output image in which each pixel of an input image is labeled to represent an extraction target (class).
  • the input image is a tomographic image forming the target image G0
  • the extraction target is the pancreas
  • the output image is an image in which the pancreas region is labeled.
  • the SS model is constructed by a convolutional neural network (CNN) such as ResNet (Residual Networks) and U-Net (U-shaped Networks).
  • CNN convolutional neural network
  • the target organ extraction unit 22 extracts the pancreas 30 from the target image G0 as shown in FIG. Note that FIG. 4 shows one tomographic image D0 included in the target image G0.
  • the extraction of target organs is not limited to using the SS model. Any method for extracting the target organ from the target image G0, such as template matching or threshold processing, can be applied.
  • the small region setting unit 23 sets a plurality of small regions in the pancreas, which is the target organ extracted from the target image G0 by the target organ extraction unit 22.
  • the small region setting unit 23 divides the pancreas 30 extracted from the target image G0 into the head, the body, and the tail so that each of the head, the body, and the tail is set as a small region. set.
  • FIG. 5 is a diagram for explaining the division of the pancreas into the head, body and tail.
  • FIG. 5 is the figure which looked at the pancreas from the front of the human body.
  • up, down, left, and right are based on the case where a human body in a standing position is viewed from the front.
  • a vein 31 and an artery 32 run vertically in parallel behind the pancreas 30 with a gap therebetween.
  • the pancreas 30 is anatomically divided into a head on the left side of the vein 31 , a body section between the vein 31 and the artery 32 , and a tail on the right side of the artery 32 .
  • the subregion setting unit 23 divides the pancreas 30 into three subregions, a head 33 , a body 34 and a tail 35 , with the vein 31 and the artery 32 as references.
  • the boundaries between the head 33, the body 34, and the tail 35 are based on the definition of boundaries described in "Pancreatic Cancer Treatment Regulations 7th Edition, Supplementary Edition, edited by the Japan Pancreas Society, page 12, September 2020.”
  • the left edge of the vein 31 (right edge of the vein 31 when the human body is viewed from the front) is defined as the boundary between the body 33 and the body 34
  • the left edge of the artery 32 (when the human body is viewed from the front)
  • the right edge of the artery 32 at the time of injection) is the boundary between the body 34 and the tail 35 .
  • the small area setting unit 23 extracts the vein 31 and the artery 32 near the pancreas 30 in the target image G0.
  • the small area setting unit 23 selects the blood vessel area and the center line ( namely, the central axis).
  • the positions and principal axis directions of a plurality of candidate points forming the center line of the blood vessel are calculated based on the values of the voxel data forming the target image G0.
  • the Hessian matrix is calculated for the target image G0, and the eigenvalues of the calculated Hessian matrix are analyzed to calculate the position information and the principal axis direction of a plurality of candidate points forming the core line of the blood vessel.
  • the small region setting unit 23 divides the pancreas 30 into a head 33 , a body 34 and a tail 35 based on the left edges (right edges when the human body is viewed from the front) of the extracted veins 31 and arteries 32 .
  • pancreas 30 is not limited to the above method.
  • pancreas 30 is divided into head 33, body 34 and tail 35 by using a machine-learned segmentation model to extract head 33, body 34 and tail 35 from pancreas 30.
  • a plurality of pairs of teacher data including a teacher 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 are prepared and segmented. You just have to learn the model.
  • FIG. 6 is a diagram for explaining another example of setting small areas. 6 is a view of the pancreas 30 viewed from the head side of the human body.
  • the small area setting unit 23 extracts the central axis 36 extending in the longitudinal direction of the pancreas 30 .
  • a method for extracting the central axis 36 a method similar to the above-described method for extracting the core lines of the veins 31 and the arteries 32 can be used.
  • the small region setting unit 23 may set small regions in the pancreas 30 by dividing the pancreas 30 into a plurality of small regions at equal intervals along the central axis 36 .
  • subregions 37A to 37C that overlap each other may be set in the pancreas 30, or spaced apart subregions such as subregions 37D and 37E may be set.
  • the small area may be set along the central axis 36 of the pancreas 30, or may be set at an arbitrary position.
  • the first evaluation value derivation unit 24 derives a first evaluation value representing the physical quantity of each of the plurality of small regions set by the small region setting unit 23 .
  • a physical quantity relating to the size of the small area is derived as the first evaluation value.
  • the representative values of the diameters of the head 33, body 34, and tail 35 of the pancreas 30 are derived as first evaluation values E11, E12, and E13.
  • the first evaluation value derivation unit 24 first sets the central axis 36 of the pancreas 30 as shown in FIG. Then, in each of the head portion 33 , body portion 34 and tail portion 35 , a plurality of cross sections perpendicular to the central axis 36 are set at regular intervals along the central axis 36 .
  • the cross section perpendicular to the central axis 36 of the pancreas 30 is nearly circular as shown in FIG. 8, but not a perfect circle.
  • the first evaluation value derivation unit 24 is oriented in a plurality of directions passing through the central axis 36 in a cross section perpendicular to the central axis 36 (for example, four directions: vertical direction, horizontal direction, lower right to upper left direction and lower left to upper right direction) , and the representative value of the lengths in multiple directions is derived as the diameter of the cross section.
  • An average value, an intermediate value, a minimum value, a maximum value, or the like can be used as the representative value.
  • the first evaluation value derivation unit 24 calculates the representative values of the diameters of a plurality of cross sections orthogonal to the central axis 36 in each of the head 33, the body 34, and the tail 35. Derive as the diameter of each of the tails 35 .
  • the representative value an average value, median value, minimum value, maximum value, or the like of the diameters of a plurality of cross sections can be used.
  • the representative values of the diameters of the head 33, body 34 and tail 35 are the first evaluation values E11, E12 and E13.
  • the first evaluation value derivation unit 24 uses the representative values of the areas of the plurality of cross sections, or the head 33, the body 34, and the The volume of tail 35 may be derived as the first evaluation value.
  • the area of the cross section can be obtained by using the number of pixels in the cross section, and the volume of the head 33, body 34 and tail 35 can be obtained by using the number of pixels in the head 33, body 34 and tail 35.
  • the first evaluation value derivation unit 24 calculates the representative value of the diameter, the representative value of the area, Alternatively, the volume may be derived as the first evaluation value.
  • the second evaluation value derivation unit 25 derives at least one second evaluation value representing the relationship between the first evaluation values E11, E12, and E13 among the plurality of small regions.
  • the second evaluation value derivation unit 25 derives an evaluation value regarding the difference in size between the plurality of small regions as the second evaluation value.
  • the second evaluation value deriving unit 25 calculates the ratio E11/E12 of the first evaluation value E11 for the head 33 to the first evaluation value E12 for the body 34 of the pancreas 30, and the tail of the pancreas
  • a ratio E12/E13 of the first evaluation value E12 of the body part 34 to the first evaluation value E13 of 35 is derived as second evaluation values E21 and E22, respectively.
  • The absolute value
  • a second evaluation value representing the relationship of the first evaluation values between adjacent small regions may be derived.
  • the second evaluation value derivation unit 25 may derive the ratio or the absolute value of the difference between the first evaluation values derived for the plurality of small regions as the second evaluation value.
  • the ratio may be, for example, the ratio of the first evaluation value of the left small region to the first evaluation value of the right small region when the human body is viewed from the front, but the ratio may be reversed.
  • may be derived as the second evaluation value.
  • the ratio of the first evaluation value of the small region 37A to the first evaluation value of the small region 37E or the absolute value of the difference is derived as the second evaluation value.
  • FIG. 9 is a diagram showing a schematic configuration of the RNN that derives the second evaluation value.
  • RNN 40 consists of encoder 41 and decoder 42 .
  • the first evaluation values E11, E12, and E13 are sequentially input to the three nodes forming the encoder 41 .
  • the decoder 42 is trained to derive a second evaluation value representing the relationship of the first evaluation values between adjacent small regions, and from the input first evaluation values E11, E12, E13, Second evaluation values E21 and E22 are derived.
  • the third evaluation value derivation unit 26 Based on the second evaluation value, the third evaluation value derivation unit 26 derives a third evaluation value that suggests the presence or absence of an abnormality in the entire pancreas, which is the target organ.
  • a tumor develops in the pancreas 30, various findings appear in the pancreas 30.
  • the pancreatic parenchyma around the tumor may swell, or the pancreatic parenchyma other than the tumor may atrophy.
  • the main pancreatic duct within the pancreas is dilated or constricted.
  • the third evaluation value derivation unit 26 derives the probability of atrophy of the pancreas as the third evaluation value based on the second evaluation values E21 and E22.
  • the third evaluation value derivation unit 26 has a derivation model that has undergone machine learning so as to derive the probability of atrophy of the entire pancreas when the second evaluation values E21 and E22 are input.
  • the derived model is constructed by a convolutional neural network similar to the SS model.
  • the derivation model of the third evaluation value derivation unit 26 may derive the presence or absence of atrophy as the third evaluation value instead of the probability of atrophy. Also, the third evaluation value deriving unit 26 compares the second evaluation values E21 and E22 with a predetermined threshold value, and at least one of the second evaluation values E21 and E22 exceeds the threshold value. In this case, the determination result that there is atrophy may be derived as the third evaluation value. In this case, when all of the second evaluation values E21 and E22 are equal to or less than the threshold value, the third evaluation value derivation unit 26 derives the determination result that there is no atrophy as the third evaluation value.
  • the presence or absence of atrophy may be derived as the third evaluation value by comparing the sum or weighted sum of the second evaluation values with a threshold value.
  • the third evaluation value deriving unit 26 derives a value of 1 when there is atrophy and a value of 0 when there is no atrophy as the third evaluation value.
  • FIG. 10 is a diagram showing a threshold table for deriving the third evaluation value.
  • the table 45 includes a threshold Th1 used when the patient is under 60 years old and a threshold Th2 used when the patient is 60 years old or older. Th1 ⁇ Th2. Note that the table 45 may be stored in the storage 13 .
  • the third evaluation value derivation unit 26 when the probability of atrophy is greater than a predetermined threshold value (for example, 0.6), or when the third evaluation value indicating the presence of atrophy is derived, a plurality of A small region from which the largest second evaluation value of the second evaluation values is derived may be specified as a region that serves as a basis for atrophy. For example, it is assumed that, of the second evaluation values E21 and E22, the probability of atrophy derived from the second evaluation value E22 is greater. In this case, the second evaluation value E22 is the ratio of the first evaluation value E12 of the body 34 to the first evaluation value E13 of the tail 35 of the pancreas. At least one of portion 34 and tail 35 is identified as the area underlying the atrophy.
  • a predetermined threshold value for example, 0.6
  • the subregion having the larger first evaluation value or the subregion having the smaller first evaluation value of the body portion 34 and the tail portion 35 may be specified as the region serving as the basis for atrophy.
  • the body part 34 having a large first evaluation value is specified as the region that serves as the basis for atrophy.
  • a small region from which the second evaluation value is derived up to the number may be specified as a region that is the basis for atrophy.
  • FIG. 11 is a diagram showing a display screen for evaluation results.
  • the evaluation result display screen 50 displays one tomographic image D0 of the target image G0 and the evaluation result 51.
  • the evaluation result 51 is the probability of atrophy, which is the third evaluation value itself.
  • 0.9 is displayed as the probability of atrophy.
  • the position of the small region in the pancreas 30 that contributes relatively high to the evaluation result based on the third evaluation value is distinguished from the position of the small region that contributes relatively low to the tomographic image D0. Is displayed.
  • the third evaluation value derivation unit 26 specifies the body part 34 as the area that serves as the basis for atrophy. Therefore, the display control unit 27 displays the tomographic image D0 by distinguishing the body part 34 from the head part 33 and the tail part 35 .
  • the body part 34 is an example of a small region with a relatively high degree of contribution to the evaluation result
  • the head 33 and the tail part 35 are examples of small regions with a relatively low degree of contribution to the evaluation result.
  • the area of the body 34 is emphasized more than the head 33 and the tail 35 are displayed.
  • the region of the body part 34 may be highlighted.
  • hatching indicates that the body part 34 of the pancreas 30 is colored.
  • the body part 34 may be emphasized by giving a color to the outline of the body part 34 or by increasing the luminance.
  • the highlighting may be switched between ON and OFF according to an instruction from the input device 15 .
  • a window 52 including an enlarged region of the pancreas 30 is displayed in a separate window from the target image G0 (tomographic image D0) in accordance with an instruction from the input device 15.
  • the pancreas 30 may be highlighted. Accordingly, it is possible to prevent the interpretation of the pancreas 30 from being hindered by the highlighted display.
  • the third evaluation value derivation unit 26 may derive a plurality of small regions as regions that serve as grounds for atrophy. be.
  • marks 53A and 53B may be given to a plurality of (two in FIG. 13) small areas.
  • the small region from which the smallest first evaluation value was derived is specified, and the diameter 54 of that small region is displayed as shown in FIG. You may make it In FIG. 13, 10 mm is displayed as the diameter 54 of the smallest subregion among the subregions that are grounds for atrophy.
  • the second evaluation value may be displayed.
  • FIG. 14 is a flow chart showing the processing performed in this embodiment.
  • the image acquisition unit 21 acquires the target image G0 from the storage 13 (step ST1), and the target organ extraction unit 22 extracts the pancreas, which is the target organ, from the target image G0 (step ST2).
  • the small region setting unit 23 sets a plurality of small regions in the pancreas, which is the target organ (step ST3).
  • the first evaluation value derivation unit 24 derives a first evaluation value representing the physical quantity in each of the plurality of small regions (step ST4).
  • the second evaluation value derivation unit 25 derives at least one second evaluation value representing the relationship of the first evaluation values between the plurality of small regions (step ST5), and derives a third evaluation value.
  • the unit 26 derives a third evaluation value indicating the presence or absence of abnormality in the entire pancreas 30, which is the target organ (step ST6).
  • the display control unit 27 displays the evaluation result based on the third evaluation value on the display 14 (step ST7), and ends the process.
  • the first evaluation value representing the physical quantity of each of the plurality of small regions is derived, and at least one second evaluation value representing the relationship of one evaluation value between the plurality of small regions.
  • An evaluation value is derived, and a third evaluation value that suggests the presence or absence of abnormality in the entire target organ is derived based on the second evaluation value. Further, the evaluation result based on the third evaluation value is displayed on the display 14. FIG. Therefore, it is possible to accurately evaluate the abnormality of the target organ by considering the relationship between the plurality of small regions in the target organ.
  • the size of the target organ such as atrophy and swelling, can be evaluated. Abnormalities caused by changes can be evaluated with high accuracy.
  • the position of a small region with a relatively high degree of contribution to the evaluation result in the target organ distinguishing it from the position of the small region with a relatively low degree of contribution, the position of the small region with a high degree of contribution to the evaluation result can be displayed. It becomes easier to recognize the position of the area. Therefore, it is possible to easily identify a position where there is a high possibility that an abnormality exists in the target organ.
  • the third evaluation value related to pancreatic atrophy is derived as a finding, but the present invention is not limited to this.
  • a third assessment value for pancreatic enlargement may be derived.
  • the third evaluation value derivation unit 26 may derive the probability of swelling or the presence or absence of swelling as the third evaluation value.
  • a plurality of small regions are set along the longitudinal direction of the pancreas, but it is not limited to this.
  • a main pancreatic duct 30A exists along the central axis 36 of the pancreas 30 within the pancreas 30 .
  • the pancreas 30 can be divided into the main pancreatic duct 30A area and the pancreatic parenchyma 30B area.
  • the main pancreatic duct 30A and the pancreatic parenchyma 30B may each be set as a small region.
  • the diameter of the main pancreatic duct 30A and the diameter of the pancreatic parenchyma 30B should be derived as the first evaluation values.
  • the diameters of the main pancreatic duct 30A and the pancreatic parenchyma 30B divide the main pancreatic duct 30A and the pancreatic parenchyma 30B into a plurality of subregions along the central axis 36 of the pancreas 30, and the diameters of the main pancreatic duct 30A and the pancreatic parenchyma 30B in the subregions are:
  • the diameters of the head portion 33, the body portion 34 and the tail portion 35 may be derived in the same manner as the diameters were derived.
  • the ratio between the diameter of the main pancreatic duct 30A and the diameter of the pancreatic parenchyma 30B or the absolute value of the difference in diameter can be used as the second evaluation value.
  • either one of the main pancreatic duct 30A and the pancreatic parenchyma 30B is divided into a plurality of regions along the central axis 36 of the pancreas 30 to set small regions, and a first evaluation value is derived for each small region.
  • first evaluation values are derived for the plurality of small regions, and at least one first evaluation value representing the relationship of the first evaluation values between the plurality of small regions is calculated.
  • An evaluation value of 2 is derived.
  • dilatation or narrowing of the main pancreatic duct 30A can be specified as a finding by the second evaluation value. Therefore, based on the second evaluation value, it is possible to derive a third evaluation value suggesting the presence or absence of dilatation or stenosis of the main pancreatic duct 30A as a finding.
  • FIG. 16 is a diagram schematically showing a cross section perpendicular to the central axis 36 of the pancreas 30. As shown in FIG. In FIG. 16, both the cross section of the pancreas 30 and the cross section of the main pancreatic duct 30A are indicated by circles. It is assumed that cross sections 60 and 61 shown in FIG. 16 represent cross sections in adjacent small regions. In cross section 60 shown in FIG.
  • the ratio of the diameter of main pancreatic duct 30A to the diameter of pancreas 30 is about 0.2, and in cross section 61, the ratio of the diameter of main pancreatic duct 30A to the diameter of pancreas 30 is about 0.5. is.
  • the second evaluation value is the absolute value of the difference between the first evaluation values
  • the second evaluation value is 0.3.
  • Such a second evaluation value serves as an index representing dilation or narrowing of the main pancreatic duct 30A. Therefore, for example, when the threshold is set to 0.1 to derive the third evaluation value, in the case of the cross sections 60 and 61 shown in FIG. can.
  • the present invention is not limited to this.
  • a physical quantity relating to the properties of the small region may be used as the first evaluation value.
  • the representative value of the CT values may be derived as the first evaluation value for each of the plurality of small regions.
  • an average value, median value, variance value, minimum value, maximum value, or the like can be used.
  • the second evaluation value can be the ratio of the first evaluation values or the absolute value of the difference between adjacent small regions.
  • a feature map consisting of feature amounts derived from the image of the small area itself or the image of the small area may be used as the first evaluation value.
  • the feature amount can be derived by filtering the image of the small area using a filter of a predetermined size having a predetermined filter coefficient.
  • the pancreas has a nearly circular cross section perpendicular to its central axis, but when an abnormality occurs, the shape of the cross section is distorted. Therefore, a shape value such as the circularity of the small region may be used as the physical quantity relating to the property of the small region, which is the first evaluation value. As the shape value, the roundness of each of the head 33, body 34 and tail 35 of the pancreas 30 can be used.
  • the central axis 36 of the pancreas 30 is set as shown in FIG. 6, the cross-sectional shape perpendicular to the central axis 36 is almost circular in a portion without abnormality.
  • the first evaluation value derivation unit 24 calculates the diameter in a plurality of directions (for example, the vertical direction, the horizontal direction, the lower right to upper left direction, and the lower left ) are derived, and the circularity is derived as a value that is half the difference between the maximum and minimum lengths in a plurality of directions.
  • the first evaluation value deriving unit 24 calculates the representative values of the roundness of a plurality of cross sections perpendicular to the central axis 36 in each of the head 33, the body 34, and the tail 35. 34 and tail 35, respectively.
  • the representative value an average value, an intermediate value, a variance value, a minimum value, a maximum value, or the like of roundness of a plurality of cross sections can be used.
  • a representative value of the circularity of each of the head portion 33, the body portion 34, and the tail portion 35 is the first evaluation value.
  • the roundness of the cross-section of the pancreas where the abnormality has occurred is smaller than the roundness of the cross-section of the normal portion. Therefore, by using the roundness as the first evaluation value and deriving the ratio of the first evaluation values or the absolute value of the difference between the adjacent small regions as the second evaluation value, the abnormalities in the pancreas can be evaluated. It is possible to derive a third evaluation value that suggests the presence or absence of
  • the target organ is the pancreas, but it is not limited to this. Any organ other than the pancreas, such as the brain, heart, lungs, and liver, can be used as the target organ.
  • a CT image is used as the target image G0, but it is not limited to this.
  • any image such as a radiation image acquired by simple imaging can be used as the target image G0.
  • a processing unit that executes various processes such as the unit 26 and the display control unit 27
  • the following various processors can be used.
  • the CPU which is a general-purpose processor that executes software (programs) and functions as various processing units, as described above
  • the above-mentioned various processors include FPGAs (Field Programmable Gate Arrays), etc.
  • Programmable Logic Device which is a processor whose configuration can be changed, ASIC (Application Specific Integrated Circuit) etc. Circuits, etc. are included.
  • One processing unit may be configured with one of these various processors, or a combination of two or more processors of the same or different type (for example, a combination of multiple FPGAs or a combination of a CPU and an FPGA). ). Also, a plurality of processing units may be configured by one processor.
  • one processor is configured by combining one or more CPUs and software, There is a form in which this processor functions as a plurality of processing units.
  • SoC System On Chip
  • the various processing units are configured using one or more of the above various processors as a hardware structure.
  • an electric circuit in which circuit elements such as semiconductor elements are combined can be used.

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