WO2020255292A1 - Bone section image analysis method and learning method - Google Patents
Bone section image analysis method and learning method Download PDFInfo
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- WO2020255292A1 WO2020255292A1 PCT/JP2019/024263 JP2019024263W WO2020255292A1 WO 2020255292 A1 WO2020255292 A1 WO 2020255292A1 JP 2019024263 W JP2019024263 W JP 2019024263W WO 2020255292 A1 WO2020255292 A1 WO 2020255292A1
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- 210000000988 bone and bone Anatomy 0.000 title claims abstract description 445
- 238000003703 image analysis method Methods 0.000 title claims abstract description 51
- 238000000034 method Methods 0.000 title claims description 34
- 238000010801 machine learning Methods 0.000 claims abstract description 85
- 239000002184 metal Substances 0.000 claims description 31
- 238000013135 deep learning Methods 0.000 claims description 7
- 238000004088 simulation Methods 0.000 abstract description 2
- 238000000605 extraction Methods 0.000 description 17
- 238000003384 imaging method Methods 0.000 description 15
- 238000004458 analytical method Methods 0.000 description 11
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- 230000037182 bone density Effects 0.000 description 8
- 230000005855 radiation Effects 0.000 description 7
- 210000000689 upper leg Anatomy 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 229910052500 inorganic mineral Inorganic materials 0.000 description 4
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- 238000005259 measurement Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000011002 quantification Methods 0.000 description 3
- 230000000052 comparative effect Effects 0.000 description 2
- 239000013078 crystal Substances 0.000 description 2
- 230000001678 irradiating effect Effects 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 239000000919 ceramic Substances 0.000 description 1
- 238000002316 cosmetic surgery Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 210000004705 lumbosacral region Anatomy 0.000 description 1
- 230000001151 other effect Effects 0.000 description 1
- 210000004197 pelvis Anatomy 0.000 description 1
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- 238000004846 x-ray emission Methods 0.000 description 1
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- 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
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
- A61B6/5217—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data extracting a diagnostic or physiological parameter from medical diagnostic data
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- A—HUMAN NECESSITIES
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- 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
- A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
- A61B6/505—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of bone
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- A61B6/5258—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise
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- G—PHYSICS
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- 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
Definitions
- the present invention relates to a bone image analysis method and a learning method, and more particularly to a bone image analysis method and a learning method for analyzing a predetermined bone region of a subject.
- a bone image analysis method and a learning method for analyzing a predetermined bone region of a subject are known.
- Such a bone image analysis method is disclosed in, for example, Japanese Patent No. 2638875.
- Japanese Patent No. 2638875 discloses a bone mineral quantitative analyzer comprising a means for generating radiation and a single crystal lattice irradiated with the radiation.
- the bone mineral quantification analyzer has two different energies by collimating (adjusting the radiations to translate each other) only the radiations having two predetermined reflection angles among the radiations reflected in the crystal lattice.
- a means for simultaneously irradiating the subject with the radiation of The bone mineral quantification analyzer scans the subject simultaneously with radiation of two different energies, so that the subject's bone mineral quantification analysis (measurement of bone density) is performed using the transmission data corresponding to each X-ray. It is configured in.
- Bone density as described above is generally measured for the bone density of the lumbar spine and femur.
- the shape of the femur varies greatly from person to person, and it is important to identify the bone region of the subject in order to carry out stable follow-up. Therefore, conventionally, in order to more accurately identify (extract) a bone region (bone image) such as a bone, it is conceivable to specify (extract) the bone region based on the learning result of machine learning. ing.
- the present invention has been made to solve the above-mentioned problems, and one object of the present invention is a subject in which a member having a brightness value larger than that of the bone is provided in a predetermined bone region. It is an object of the present invention to provide a bone image analysis method and a learning method capable of facilitating the analysis of a bone portion on a captured image.
- the bone image analysis method in the first aspect of the present invention includes a step of acquiring a plurality of bone region images in which a predetermined bone region is displayed, and a plurality of bone region images.
- a step of acquiring a first learning input image by adding a first simulated member image that simulates a predetermined member having a brightness value larger than that of the bone to a part of the bone region image.
- the learning method includes a step of acquiring a plurality of bone region images in which a predetermined bone region is displayed, and a partial bone region of the plurality of bone region images.
- a step of acquiring a learning input image by adding a simulated member image simulating a predetermined member having a brightness value larger than that of the bone portion to the image, and a predetermined bone region and simulated member image in the learning input image.
- the step of acquiring the label image including the correct answer information of the position where is displayed, and the learning input image and the label image the image is taken by an X-ray apparatus, and a predetermined bone region and a predetermined member are displayed. It includes a step of performing machine learning for extracting a predetermined bone region and a predetermined member on the captured image.
- a first learning input image (learning input image) in which a simulated member image simulating a predetermined member having a brightness value larger than that of the bone portion is added to the bone region image is provided.
- Machine learning is carried out using it.
- machine learning can be performed using a simulated first learning input image (learning input image) in which the predetermined member is actually provided in the predetermined bone region.
- the simulated first learning input image (for learning) By using the input image), machine learning for extracting a predetermined bone region and a predetermined member can be performed.
- a predetermined bone region (and a predetermined member) can be appropriately extracted from a photographed image of a subject in which a predetermined member having a brightness value larger than that of the bone portion is provided in the predetermined bone region. ..
- a predetermined member having a brightness value larger than that of the bone portion is provided in the predetermined bone region.
- the X-ray imaging apparatus 100 includes an X-ray irradiation unit 1, an X-ray detection unit 2, an image processing unit 3, and a control unit 4. Further, the X-ray photographing apparatus 100 includes a display unit 5 for displaying the processed image on the image processing unit 3.
- the X-ray irradiation unit 1 irradiates the subject T with X-rays.
- the X-ray detection unit 2 detects the X-rays emitted from the X-ray irradiation unit 1 to the subject T.
- the X-ray imaging apparatus 100 is used, for example, to calculate (measure) the bone density of the bone region A (see FIG. 2) of the subject T.
- DEXA Dual-Energy X
- the ray Absorptiometri is used.
- the bone region A is a region including the femur and the pelvis. That is, the bone region A exists in each of the left half body and the right half body of the subject T.
- the bone region A is an example of the "predetermined bone region" in the claims.
- the X-ray irradiation unit 1 includes an X-ray source 1a.
- the X-ray source 1a is an X-ray tube that is connected to a high voltage generating portion (not shown) and generates X-rays when a high voltage is applied.
- the X-ray source 1a is arranged so that the X-ray emission direction is directed toward the detection surface of the X-ray detection unit 2.
- the X-ray detection unit 2 detects the X-rays emitted from the X-ray irradiation unit 1 and transmitted through the subject T, and outputs a detection signal according to the detected X-ray intensity.
- the X-ray detector 2 is composed of, for example, an FPD (Flat Panel Detector).
- the image processing unit 3 includes an image acquisition unit 3a, a machine learning base region extraction unit 3b, and an analysis unit 3c.
- Each of the image acquisition unit 3a, the machine learning base region extraction unit 3b, and the analysis unit 3c is a functional block as software in the image processing unit 3. That is, each of the image acquisition unit 3a, the machine learning base region extraction unit 3b, and the analysis unit 3c is configured to function based on the command signal of the control unit 4.
- the image acquisition unit 3a acquires the captured image 10 (see FIG. 2) of the subject T based on the X-rays detected by the X-ray detection unit 2.
- the captured image 10 is an energy subtraction image acquired by calculating the difference between the acquired images using X-rays of two different energies.
- the captured image 10 may be an X-ray image or a DRR (Digitally Reduced Radiograph) image created from the CT image data of the subject T.
- the machine learning base region extraction unit 3b determines on the captured image 10 based on the learning result of machine learning in the learning device 200 in the captured image 10 acquired based on the X-rays detected by the X-ray detection unit 2. It is configured to extract the area of. Specifically, in the first embodiment, deep learning is used as machine learning.
- the analysis unit 3c is configured to calculate the circularity and the bone density of the region designated by the technician in the captured image 10.
- the learning device 200 is configured to perform machine learning for extracting the bone region A on the captured image 10 (see FIG. 2) on which the bone region A (see FIG. 2) is displayed. Further, the learning device 200 is configured to perform machine learning for extracting the bone region A and the member 300 on the photographed image 10 (see FIG. 3) in which the bone region A and the member 300 are displayed. Has been done.
- the member 300 is an example of a "predetermined member" in the claims.
- the member 300 is a member having a brightness value larger than that of the bone portion.
- the member 300 includes metal, at least in part, disposed within the bone region A.
- the member 300 may be a metal indwelling object used in plastic surgery, such as an artificial joint, a fixing plate, and a screw.
- the bone image analysis method includes a step of acquiring a plurality of (for example, 100) bone region images 20 (see FIG. 5) performed in step 101.
- the plurality of bone region images 20 are images in which the bone region A is displayed.
- the plurality of bone region images 20 may be images acquired by the X-ray imaging device 100 or images acquired by other devices.
- the bone region image 20 may be any of an energy subtraction image, an X-ray image, and a DRR image. In the case of a DRR image, a DRR image of only the bone may be used.
- images taken at different tube voltages such as low voltage and high voltage may be included.
- the step of acquiring the bone region image 20 is on the right side where the bone region A of one of the left and right (the right side as an example in the first embodiment) is displayed.
- the step of acquiring the bone region image 21 as the bone region image 20 is included.
- the left bone region image 22 on which the bone region A of the other side (that is, the left side) of the left and right is displayed is flipped left and right to obtain the inverted posterior bone region image 23.
- Including the step of acquiring as the bone region image 20 That is, the left bone region image 22 in which the left bone region A is displayed is flipped left and right so that the right bone region A is displayed in a simulated posterior bone region image 23.
- the right bone region image 21 is an example of the “one-sided bone image” in the claims.
- the left bone region image 22 and the inverted posterior bone region image 23 are examples of the “other side bone region pre-inversion image” and the “other side bone region post-inversion image” in the claims, respectively.
- the bone image analysis method includes a step of acquiring a plurality of learning input images 30 (see FIG. 6) performed in step 102.
- the learning input image 30 is an example of the "first learning input image” in the claims.
- a simulated member image 300a simulating the member 300 is added to a part of the bone region images 20 among the plurality of bone region images 20.
- the learning input image 30 is acquired.
- the step of acquiring the learning input image 30 is arranged in a part of the bone region image 20 of the plurality of bone region images 20, and at least a part of the step is arranged inside the bone region A.
- the step includes adding a simulated member image 300a simulating metal.
- the simulated member image 300a is added to each of the bone region images 20 of, for example, about 30% of the plurality of bone region images 20.
- the ratio of 30% is an example and is not limited to this.
- the shape of the simulated member image 300a shown in FIG. 6 is an example, and may be, for example, a circular shape or a triangular shape.
- the simulated member image 300a is an example of the "first simulated member image" in the claims.
- the step of adding the simulated member image 300a simulating the metal includes a step of adding the simulated member image 300a having a brightness value substantially equal to the brightness value of the metal to the bone region image 20.
- the brightness value of the simulated member image 300a is randomly selected (set) from a range of predetermined brightness values considered as metal.
- the input image 32 is acquired as a plurality of learning input images 30. That is, the simulated member image 300a is added to each of the plurality of learning input images 30 (31, 32) in which the directions of the bone region A are all aligned.
- the bone region image 20 to which the simulated member image 300a is added may be only the right bone region image 21 or only the inverted bone region image 23.
- the right-side learning input image 31 and the left-side learning input image 32 are examples of the “one-sided learning image” and the “other-side learning image” in the claims, respectively.
- the right bone region image 21 and the inverted posterior bone region image 23 will be described as the bone region image 20 without distinction. Further, the right side learning input image 31 and the left side learning input image 32 will be described as the learning input image 30 without distinction.
- the step of acquiring the learning input image 30 is the brightness value, shape, position, and number of the simulated member image 300a for each of the plurality of bone region images 20 to which the simulated member image 300a is added.
- a step of acquiring a plurality of learning input images 30 is included by adding a simulated member image 300a to each of the plurality of bone region images 20 so that at least one of them is different from each other.
- the brightness value, shape, position, and number of the simulated member image 300a to be added for each of the plurality of bone region images 20 are randomly set by the image processing unit 3 (image acquisition unit 3a).
- the image processing unit 3 image acquisition unit 3a so that at least one of the brightness value, shape, position, and number of the simulated member image 300a to be added is different between the bone region images 20. It is set (adjusted). For example, the shape and number of the simulated member images 300a are different between the right side learning input image 31 and the left side learning input image 32 in FIG.
- the bone image analysis method includes a step of acquiring the label image 40 (see FIG. 7) performed in step 103.
- the label image 40 includes correct answer information 400 at a position where the bone region A and the simulated member image 300a are displayed in the learning input image 30.
- the label image 40 is an image manually generated (acquired) by an engineer based on each of the plurality of learning input images 30.
- the label image 40 and the correct answer information 400 are examples of the "first label image" and the "first correct answer information" in the claims, respectively.
- the step of acquiring the label image 40 includes a step of acquiring the label image 40 in which a common correct answer value is given to the positions corresponding to the bone region A and the simulated member image 300a on the label image 40. Specifically, a common correct answer value 1 is given to each of the positions (coordinates) on the label image 40 corresponding to the bone region A and the simulated member image 300a on the learning input image 30.
- the remaining portion (background portion) of the label image 40 is in a state of value 0. That is, the label image 40 is binarized so as to be divided into a region corresponding to the bone region A and the simulated member image 300a and a region corresponding to the remaining portion (background portion).
- the bone image analysis method (learning method) is performed in step 103, and the simulated member image 300a of the plurality of bone region images 20 is not added to the bone.
- a step of acquiring a label image 41 (see FIG. 8) including correct answer information 410 (see FIG. 8) at a position where the bone region A is displayed is provided.
- the bone region image 20 to which the simulated member image 300a is not added is about 70% of all the bone region images 20.
- the label image 41 and the correct answer information 410 are examples of the "third label image" and the "third correct answer information" in the claims, respectively.
- the correct answer value 1 is given to the position (coordinates) on the label image 41 corresponding to the bone region A on the bone region image 20 (the simulated member image 300a is not added).
- the remaining portion (background portion) of the label image 41 is in a state of value 0. That is, the label image 41 is binarized so as to be divided into a region corresponding to the bone region A and a region corresponding to the remaining portion (background portion).
- the bone image analysis method includes a step of performing machine learning, which is performed in step 104.
- the machine learning in this step 104 uses the learning input image 30 and the label image 40 to display the bone region A and the member 300 on the captured image 10 (see FIG. 3).
- This is a step of performing machine learning for extracting the part region A and the member 300.
- this machine learning is carried out by using a plurality of sets of the learning input image 30 and the label image 40 corresponding to each other as learning data.
- the pair of the learning input image 30 and the label image 40 corresponding to each other is composed of one learning input image 30 and the label image 40 generated (acquired) from the one learning input image 30. Means the pair to be.
- the captured image 10 is an image captured by the X-ray imaging apparatus 100.
- machine learning is performed using a set of the learning input image 30 and the label image 40, and a set of the bone region image 20 and the label image 41 to which the simulated member image 300a is not added.
- both the bone region image 20 (learning input image 30) to which the simulated member image 300a is added and the bone region image 20 to which the simulated member image 300a is not added are input data for machine learning. It is used as.
- the ratio of the pair of the learning input image 30 and the label image 40 to the pair of the bone region image 20 and the label image 41 to which the simulated member image 300a is not added is, for example, about 3: 7.
- the step 104 for carrying out machine learning includes a step for carrying out re-learning.
- the bone region image 20 that is the source of the learning input image 30 has a brightness value, shape, position, and number that are different from the simulated member image 300a (see FIG. 6) of the learning input image 30.
- a learning input image 50 (see FIG. 9) is used to which a simulated member image 300b (see FIG. 9) simulating the member 300, which is different from at least one of them, is added.
- the label image 60 (see FIG. 10) including the correct answer information 600 of the position where the bone region A and the simulated member image 300b are displayed in the learning input image 50 is used.
- Re-learning using the learning input image 50 and the label image 60 is performed after machine learning using the learning input image 30 (see FIG. 7) and the label image 40 (see FIG. 7).
- the label image 60 and the learning input image 50 are examples of the “second label image” and the “second learning input image” in the claims, respectively.
- the correct answer information 600 and the simulated member image 300b are examples of the "second correct answer information" and the "second simulated member image” in the claims, respectively.
- the correct answer value common to each of the positions (coordinates) on the label image 60 corresponding to the bone region A and the simulated member image 300b on the learning input image 50. 1 is given.
- the remaining portion (background portion) of the label image 60 is in a state of value 0. That is, the label image 60 is binarized so as to be divided into a region corresponding to the bone region A and the simulated member image 300b and a region corresponding to the remaining portion (background portion).
- the re-learning is repeated thousands of times after machine learning (and learning using the label image 41) using the learning input image 30 (see FIG. 7) and the label image 40 (see FIG. 7). Will be.
- each of the plurality of bone region images 20 to which the simulated member image 300b is added at least one of the brightness value, shape, position, and number of the simulated member image 300b is relearned each time in thousands of times. It is adjusted by the image processing unit 3 (image acquisition unit 3a) so as to be changed.
- the bone image analysis method includes a step of acquiring an image to be image-extracted (segmentation), which is performed in step 105.
- the photographed image 10 (see FIGS. 2 and 3) is acquired by photographing the subject T with the X-ray photographing apparatus 100.
- the captured image 10 in which the left bone region A is displayed is acquired, the right bone region A is displayed by reversing the captured image 10 in which the left bone region A is displayed. Get an image that looks like it is.
- the orientation of the bone region A in the image to be image-extracted (segmentation) can be aligned with the orientation of the bone region A in the learning image (see FIGS. 7 and 8) used for machine learning. It is possible.
- the bone image analysis method is performed on the captured image 10 based on the learning result of the machine learning of step 104 performed in step 106.
- a step of extracting (segmenting) A and the member 300 is provided. That is, the bone region A and the member 300 are extracted (segmented) on the captured image 10 based on the learning results of thousands of re-learning times.
- the step of extracting the bone region A and the member 300 is based on the learning result of machine learning (including re-learning), and the bone region A is displayed on the captured image 10. And the step of integrally extracting the member 300. That is, the bone region A and the member 300 are not distinguished from each other, and the region corresponding to the bone region A and the member 300 on the photographed image 10 (the black-painted portion in FIG. 11A) is extracted. .. As a result, on the captured image 10, the region corresponding to the bone region A and the member 300 and the region corresponding to the remaining region (background portion) (white-painted portion in FIG. 11A) are distinguished. To. As shown in the comparative example of FIG. 11B, in the conventional method (method of learning only the bone portion), the member 300 and the bone portion separated from the member 300 are extracted. However, the bone around the member 300 has not been extracted.
- the bone image analysis method includes a step of analyzing the image, which is performed in step 107. Specifically, an arbitrary region is selected on the image in the bone region A extracted in step 106, and the selection result is accepted by the image processing unit 3. Then, in the selected analysis region, the bone density is measured (calculated), the circularity is measured (calculated), and the like.
- the bone region A and the member 300 separately, for example, by performing extraction using a rule base based on the brightness value in the image-extracted (segmented) region, the bone region It is possible to extract only A.
- the bone image analysis method includes a step of acquiring a plurality of bone region images 20 in which the bone region A is displayed, and one of the plurality of bone region images 20.
- a step of acquiring a learning input image 30 by adding a simulated member image 300a simulating a member 300 having a brightness value larger than that of the bone portion to the bone region image 20 of the portion is provided.
- the bone image analysis method includes a step of acquiring a label image 40 including correct answer information 400 at a position where the bone region A and the simulated member image 300a are displayed in the learning input image 30.
- the bone portion is photographed by the X-ray imaging apparatus 100 using the learning input image 30 and the label image 40, and the bone portion is displayed on the captured image 10 in which the bone region A and the member 300 are displayed.
- a step of performing machine learning for extracting the region A and the member 300 is provided.
- the bone image analysis method includes a step of extracting the bone region A and the member 300 on the captured image 10 based on the learning result of machine learning. As a result, machine learning can be performed using a simulated image (learning input image 30) in which the member 300 is actually provided in the bone region A.
- the bone portion Machine learning can be performed to extract the region A and the member 300.
- the bone region A (and the member 300) can be appropriately extracted from the photographed image 10 of the subject T in which the member 300 having a brightness value larger than that of the bone portion is provided in the bone region A.
- the step of acquiring the learning input image 30 is a part of the bone region image 20 of the plurality of bone region images 20, and at least a part of the bone region image 20.
- a step of adding a simulated member image 300a simulating a metal arranged inside the region A is included. Thereby, the bone region A (and the metal) can be appropriately extracted from the photographed image 10 in which the metal is arranged in the bone region A.
- the simulated member image 300a simulating the metal in the step of adding the simulated member image 300a simulating the metal, the simulated member image 300a having a brightness value substantially equal to the brightness value of the metal is added to the bone region image 20. Includes steps to add.
- machine learning can be performed by the learning input image 30 under conditions similar to those when the metal is actually arranged in the bone region A.
- the bone region A (and the metal) can be more appropriately extracted from the photographed image 10 in which the metal is arranged in the bone region A.
- the simulated member image 300a is added.
- Each of the plurality of bone region images 20 has a simulated member so that at least one of the brightness value, shape, position, and number of the simulated member image 300a differs from each other for each of the plurality of bone region images 20.
- the step of acquiring a plurality of learning input images 30 by adding the image 300a is included.
- machine learning can be performed using different learning input images 30, so that the learning input images 30 used for machine learning can be varied.
- machine learning can be performed using more types of learning input images 30, and the accuracy of machine learning can be improved. Therefore, the member 300 is provided in the bone region A for imaging.
- the bone region A (and member 300) can be more appropriately extracted from the image 10.
- the step of acquiring the label image 40 a label in which a common correct answer value is given to the positions corresponding to the bone region A and the simulated member image 300a on the label image 40.
- the step of acquiring the image 40 is included.
- the step of extracting the bone region A and the member 300 includes a step of integrally extracting the bone region A and the member 300 on the captured image 10 based on the learning result of machine learning.
- machine learning can be performed based on a smaller number of correct answer values than when different correct answer values are given to the bone region A and the simulated member image 300a.
- machine learning in the learning device 200 can be relatively simplified.
- the bone region image 20 which is the source of the learning input image 30 is different from the simulated member image 300a of the learning input image 30.
- a step of re-learning after learning using the learning input image 30 and the label image 40 by using the label image 60 including the correct answer information 600 of the position where the member image 300b is displayed is included.
- the step of extracting the bone region A and the member 300 based on the learning result of machine learning is a step of extracting the bone region A and the member 300 on the captured image 10 based on the re-learned learning result. including.
- the bone region image 20 that is the source of the learning input image 30 in the re-learning, the bone region image 20 is prepared (memorized) in advance as compared with the case where more bone region images 20 are learned at one time. It is possible to suppress an increase in the number of bone region images 20.
- the bone region A is displayed in the bone region image 20 to which the simulated member image 300a of the plurality of bone region images 20 is not added.
- a step of acquiring the label image 41 including the correct answer information 410 of the position to be performed is provided.
- machine learning is performed using a set of the learning input image 30 and the label image 40, and a set of the bone region image 20 and the label image 41 to which the simulated member image 300a is not added. Includes steps to perform.
- the bone region A (and the member 300) can be extracted from the captured image 10 in which the member 300 is provided in the bone region A, and the captured image 10 in which only the bone region A is displayed can be extracted. Bone region A can be extracted.
- the step of acquiring the plurality of bone region images 20 is left or right when the bone region A is present in each of the left half body and the right half body of the subject T.
- a right-side learning input image 31 in which a simulated member image 300a is added to a right-side bone region image 21 in which one bone region A is displayed, and a left-side bone in which the other bone region A of the left and right is displayed.
- the step includes a step of acquiring the left side learning input image 32 in which the simulated member image 300a is added to the inverted posterior bone region image 23 in which the part area image 22 is flipped left and right as the learning input image 30.
- the left side bone region image 22 is flipped left and right to acquire the left side learning input image 32 simulated so that the right side bone region A is displayed, whereby the right side learning input image 31 And the orientation of the bone region A in each of the left learning input images 32 can be aligned.
- the orientations are unified (that is, the learning conditions are unified) based on the learning data (learning input image 30). Since learning can be performed, the learning efficiency of machine learning can be made higher than in the case of learning separately on the left and right sides.
- machine learning includes deep learning.
- the extraction accuracy of the extraction target region by deep learning is relatively high, so that the bone region A (and the member 300) can be accurately extracted on the captured image 10.
- the learning method includes a step of acquiring a plurality of bone region images 20 in which the bone region A is displayed, and a part of the plurality of bone region images 20.
- the learning method includes a step of acquiring a label image 40 including correct answer information 400 at a position where the bone region A and the simulated member image 300a are displayed in the learning input image 30.
- the bone region A is captured on the captured image 10 captured by the X-ray imaging device 100 using the learning input image 30 and the label image 40, and the bone region A and the member 300 are displayed. And a step of performing machine learning to extract the member 300.
- machine learning can be performed using a simulated image (learning input image 30) in which the member 300 is actually provided in the bone region A.
- the bone portion Machine learning can be performed to extract the region A and the member 300.
- the bone region A (and the member 300) can be appropriately extracted from the photographed image 10 of the subject T in which the member 300 having a brightness value larger than that of the bone portion is provided in the bone region A.
- a learning method capable of facilitating the analysis of the bone portion on the photographed image 10 of the subject T in which the member 300 having a brightness value larger than that of the bone portion is provided in the bone region A is provided. can do.
- the configuration of the bone image analysis method (learning method) according to the second embodiment will be described with reference to FIGS. 12 to 15.
- the bone image analysis method (learning method) of the second embodiment unlike the first embodiment in which extraction is performed without distinguishing between the bone region A and the member 300, the bone region A and the member 300 are separated. Extraction is performed separately.
- the same configuration as that of the first embodiment is illustrated with the same reference numerals as those of the first embodiment, and the description thereof will be omitted.
- the machine learning base region extraction unit 3b performs machine learning in the learning device 210 in the captured image 10 acquired based on the X-rays detected by the X-ray detection unit 2. It is configured to extract a predetermined region on the captured image 10 based on the learning result of.
- the bone image analysis method includes a step of acquiring a label image 70 (see FIG. 14), which is performed in step 113.
- the label image 70 includes correct answer information 700 (see FIG. 14) at a position where the bone region A and the simulated member image 300a are displayed in the learning input image 30.
- the label image 70 and the correct answer information 700 are examples of the "first label image” and the "first correct answer information" in the claims, respectively.
- the step of acquiring the label image 70 different correct answer values are given to the positions corresponding to the bone region A and the simulated member image 300a on the label image 70.
- the step of acquiring the label image 70 is included. Specifically, the correct answer value 1 is given to the position (coordinates) on the label image 70 corresponding to the bone region A on the learning input image 30, and the simulated member image 300a on the learning input image 30 is supported.
- the correct answer value 2 is given to the position (coordinates) on the label image 70.
- the correct answer information 700 includes the correct answer information 700a at the position where the bone region A is displayed in the learning input image 30, and the correct answer information 700b at the position where the simulated member image 300a is displayed in the learning input image 30. ..
- the remaining portion (background portion) of the label image 70 is in a state of value 0. That is, the label image 70 is ternary so as to be divided into a region corresponding to the bone region A, a region corresponding to the simulated member image 300a, and a region corresponding to the remaining portion (background portion).
- the correct answer information 700a and the correct answer information 700b are examples of the "first correct answer information" in the claims.
- the bone region A on the captured image 10 is based on the learning result of machine learning (including re-learning). And the step of extracting the members 300 individually. That is, the bone region A and the member 300 are distinguished from each other, and the region corresponding to the bone region A on the photographed image 10 (the portion of the diagonal line downward in the left direction of FIG. Each of the 15 (a) downward-sloping diagonal lines) is extracted individually. As a result, on the captured image 10, the region corresponding to the bone region A, the region corresponding to the member 300, and the region corresponding to the remaining region (background portion) (the white-painted portion of FIG. 15A). ) And are distinguished (extracted individually from each other).
- the bone image analysis method includes a step of analyzing the image, which is performed in step 117. That is, an arbitrary region of the bone region A is selected on the image in which the region corresponding to the bone region A, the region corresponding to the member 300, and the region corresponding to the remaining region (background portion) are distinguished. The selection result is accepted by the image processing unit 3. Then, in the selected analysis region, the bone density is measured (calculated), the circularity is measured (calculated), and the like.
- the step of acquiring the label image 70 is a label in which different correct answer values are given to the positions corresponding to the bone region A and the simulated member image 300a on the label image 70.
- the step of acquiring the image 70 is included.
- the step of extracting the bone region A and the member 300 includes a step of individually extracting the bone region A and the member 300 on the captured image 10 based on the learning result of machine learning. As a result, the boundary between the bone region A and the member 300 can be extracted, so that it is easy to select and analyze only the bone portion on the image in which the bone region A and the member 300 are individually extracted. Can be transformed into.
- a simulated member image 300a (first simulated member image) simulating a metal arranged inside a bone region A (predetermined bone region) is used.
- a simulated member image simulating a metal that is, a metal not embedded in the bone
- a metal that is, a metal not embedded in the bone
- a simulated member image 300a (first simulated member image) simulating a metal
- a simulated member image simulating a member other than metal for example, ceramic
- a simulated member image having a brightness value substantially equal to the brightness value of the member other than the metal is added to the bone region image 20.
- the present invention is not limited to this.
- the number of pairs of the learning input image 30 (first learning input image) and the label image 40 (first label image) used for one machine learning is increased as compared with the case of re-learning. Learning may be performed only once.
- the left bone region image 22 (the image before the other side bone reversal) in which the left bone region A (predetermined bone region) is displayed is flipped left and right.
- the right bone region image 21 (one-sided bone region image) on which the right bone region A (predetermined bone region) is displayed may be flipped horizontally.
- the left bone region image 22 (the image before the other side bone reversal) in which the left bone region A (predetermined bone region) is displayed is flipped left and right.
- An example of adding a simulated member image 300a (first simulated member image) to the posterior bone region image 23 (image after flipping the other side bone) has been shown, but the present invention is not limited to this.
- the simulated member image 300b (second simulated member image) is added to the bone region image 20 that is the source of the learning input image 30 (first learning input image).
- the present invention is not limited to this.
- the simulated member image 300b (second simulated member image) may be added to at least a part of the bone region image 20 to which the simulated member image 300a of the plurality of bone region images 20 is not added.
- the bone region A predetermined bone region
- the member 300 predetermined member
- the remaining portion background portion
- the present invention is not limited to this.
- the member 300 (predetermined member) and the background portion may not be distinguished, and the bone region A (predetermined bone region), the member 300 (predetermined member), and the background portion may be extracted individually. Good.
- the bone region A is a region including the femur, but the present invention is not limited to this.
- the bone region A may be a region of the bone other than the femur.
- the predetermined bone portion is photographed by an X-ray apparatus using the first learning input image and the first label image, and the predetermined bone portion is displayed on the captured image in which the predetermined bone region and the predetermined member are displayed.
- a step of performing machine learning to extract a region and the predetermined member, and A bone image analysis method comprising a step of extracting the predetermined bone region and the predetermined member on the captured image based on the learning result of the machine learning.
- the step of acquiring the first learning input image is a metal in which a part of the plurality of bone region images is arranged in the bone region image, and at least a part of the metal is arranged inside the predetermined bone region.
- the step of adding the first simulated member image simulating the metal includes a step of adding the first simulated member image having a brightness value substantially equal to the brightness value of the metal to the bone region image.
- the bone image analysis method according to any one of items 1 to 3, further comprising a step of acquiring a plurality of the first learning input images by adding the image.
- the step of acquiring the first label image is to obtain the first label image in which a common correct answer value is given to the predetermined bone region and the position corresponding to the first simulated member image on the first label image. Including the steps to get In the step of extracting the predetermined bone region and the predetermined member, the predetermined bone region and the predetermined member are integrally extracted on the photographed image based on the learning result of the machine learning.
- the bone image analysis method according to any one of items 1 to 3, which comprises a step.
- the step of acquiring the first label image is to obtain the first label image in which different correct answer values are given to the predetermined bone region and the positions corresponding to the first simulated member image on the first label image.
- Including the steps to get The step of extracting the predetermined bone region and the predetermined member is a step of individually extracting the predetermined bone region and the predetermined member on the photographed image based on the learning result of the machine learning.
- the bone image analysis method according to any one of items 1 to 3, which comprises.
- the brightness value, shape, and position of the bone region image that is the source of the first learning input image and the first simulated member image of the first learning input image are obtained.
- a second learning input image to which a second simulated member image simulating the predetermined member, which is different in at least one of the numbers, and the predetermined bone region in the second learning input image are obtained.
- the step of extracting the predetermined bone region and the predetermined member based on the learning result of the machine learning is the step of extracting the predetermined bone region and the predetermined member on the captured image based on the relearned learning result.
- the bone image analysis method according to any one of items 1 to 3, which comprises a step of extracting a predetermined member.
- a third label image including the third correct answer information of the position where the predetermined bone region is displayed is acquired.
- the step of performing the machine learning is a set of the first learning input image and the first label image, and the bone region image to which the first simulated member image is not added and the third label image.
- the bone image analysis method according to any one of items 1 to 3, which comprises a step of performing the machine learning using a set.
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Abstract
Description
(X線撮影装置の構成)
図1に示すように、X線撮影装置100は、X線照射部1と、X線検出部2と、画像処理部3と、制御部4とを備えている。また、X線撮影装置100は、画像処理部3に処理された画像を表示する表示部5を備えている。 [First Embodiment]
(Configuration of X-ray imaging device)
As shown in FIG. 1, the
学習装置200は、骨部領域A(図2参照)が表示された撮影画像10(図2参照)上において、骨部領域Aを抽出するための機械学習を実施するように構成されている。また、学習装置200は、骨部領域Aおよび部材300が表示されている撮影画像10(図3参照)上において、骨部領域Aおよび部材300を抽出するための機械学習を実施するように構成されている。なお、部材300は、請求の範囲の「所定の部材」の一例である。 (Configuration of learning device)
The
次に、図4~図11を参照して、X線撮影装置100における骨部画像解析方法、および、学習装置200における学習方法について説明する。 (Bone image analysis method and learning method)
Next, the bone image analysis method in the
第1実施形態では、以下のような効果を得ることができる。 (Effect of the first embodiment)
In the first embodiment, the following effects can be obtained.
次に、図12~図15を参照して、第2実施形態による骨部画像解析方法(学習方法)の構成について説明する。第2実施形態の骨部画像解析方法(学習方法)では、骨部領域Aと部材300とを区別せずに抽出を行う上記第1実施形態とは異なり、骨部領域Aと部材300とを区別して抽出が行われる。なお、上記第1実施形態と同様の構成は、第1実施形態と同じ符号を付して図示するとともに説明を省略する。 [Second Embodiment]
Next, the configuration of the bone image analysis method (learning method) according to the second embodiment will be described with reference to FIGS. 12 to 15. In the bone image analysis method (learning method) of the second embodiment, unlike the first embodiment in which extraction is performed without distinguishing between the bone region A and the
図12に示すように、第2実施形態では、機械学習ベース領域抽出部3bは、X線検出部2により検出されたX線に基づいて取得された撮影画像10において、学習装置210における機械学習の学習結果に基づいて、撮影画像10上における所定の領域を抽出するように構成されている。 (Configuration of X-ray imaging device)
As shown in FIG. 12, in the second embodiment, the machine learning base
次に、図13~図15を参照して、X線撮影装置100における骨部画像解析方法、および、学習装置210における学習方法について説明する。 (Bone image analysis method and learning method)
Next, the bone image analysis method in the
第2実施形態では、以下のような効果を得ることができる。 (Effect of the second embodiment)
In the second embodiment, the following effects can be obtained.
なお、今回開示された実施形態は、すべての点で例示であって制限的なものではないと考えられるべきである。本発明の範囲は、上記した実施形態の説明ではなく請求の範囲によって示され、さらに請求の範囲と均等の意味および範囲内でのすべての変更(変形例)が含まれる。 (Modification example)
It should be noted that the embodiments disclosed this time are exemplary in all respects and are not considered to be restrictive. The scope of the present invention is shown by the claims rather than the description of the above-described embodiment, and further includes all modifications (modifications) within the meaning and scope equivalent to the claims.
上記した例示的な実施形態は、以下の態様の具体例であることが当業者により理解される。 [Aspect]
It will be understood by those skilled in the art that the above exemplary embodiments are specific examples of the following embodiments.
所定の骨部領域が表示された複数の骨部領域画像を取得するステップと、
前記複数の骨部領域画像のうちの一部の前記骨部領域画像に、骨部よりも輝度値が大きい所定の部材を模擬した第1模擬部材画像を付加することにより、第1学習用入力画像を取得するステップと、
前記第1学習用入力画像において前記所定の骨部領域および前記第1模擬部材画像が表示される位置の第1正解情報を含む第1ラベル画像を取得するステップと、
前記第1学習用入力画像と前記第1ラベル画像とを用いて、X線撮影装置により撮影され、前記所定の骨部領域および前記所定の部材が表示された撮影画像上において前記所定の骨部領域および前記所定の部材を抽出するための機械学習を実施するステップと、
前記機械学習の学習結果に基づいて、前記撮影画像上において、前記所定の骨部領域および前記所定の部材を抽出するステップと、を備える、骨部画像解析方法。 (Item 1)
A step of acquiring a plurality of bone region images in which a predetermined bone region is displayed, and
Input for first learning by adding a first simulated member image that simulates a predetermined member having a brightness value larger than that of the bone to the bone region image that is a part of the plurality of bone region images. Steps to get the image and
A step of acquiring a first label image including the first correct answer information of the predetermined bone region and the position where the first simulated member image is displayed in the first learning input image.
The predetermined bone portion is photographed by an X-ray apparatus using the first learning input image and the first label image, and the predetermined bone portion is displayed on the captured image in which the predetermined bone region and the predetermined member are displayed. A step of performing machine learning to extract a region and the predetermined member, and
A bone image analysis method comprising a step of extracting the predetermined bone region and the predetermined member on the captured image based on the learning result of the machine learning.
前記第1学習用入力画像を取得するステップは、前記複数の骨部領域画像のうちの一部の前記骨部領域画像に、少なくとも一部が前記所定の骨部領域の内部に配置された金属を模擬した前記第1模擬部材画像を付加するステップを含む、項目1に記載の骨部画像解析方法。 (Item 2)
The step of acquiring the first learning input image is a metal in which a part of the plurality of bone region images is arranged in the bone region image, and at least a part of the metal is arranged inside the predetermined bone region. The bone image analysis method according to
前記金属を模擬した前記第1模擬部材画像を付加するステップは、前記金属の輝度値と略等しい輝度値を有する前記第1模擬部材画像を、前記骨部領域画像に付加するステップを含む、項目2に記載の骨部画像解析方法。 (Item 3)
The step of adding the first simulated member image simulating the metal includes a step of adding the first simulated member image having a brightness value substantially equal to the brightness value of the metal to the bone region image. 2. The bone image analysis method according to 2.
前記第1学習用入力画像を取得するステップは、複数の前記骨部領域画像の各々に前記第1模擬部材画像を付加する場合に、前記第1模擬部材画像が付加される前記複数の骨部領域画像ごとに、前記第1模擬部材画像の輝度値、形状、位置、および、数のうちの少なくとも1つが互いに異なるように、前記複数の骨部領域画像の各々に前記第1模擬部材画像を付加することによって、複数の前記第1学習用入力画像を取得するステップを含む、項目1~3のいずれか1項に記載の骨部画像解析方法。 (Item 4)
In the step of acquiring the first learning input image, when the first simulated member image is added to each of the plurality of bone region images, the plurality of bones to which the first simulated member image is added. The first simulated member image is provided in each of the plurality of bone region images so that at least one of the brightness value, shape, position, and number of the first simulated member image is different from each other for each region image. The bone image analysis method according to any one of
前記第1ラベル画像を取得するステップは、前記第1ラベル画像上において前記所定の骨部領域および前記第1模擬部材画像に対応する位置に共通の正解値が付与された前記第1ラベル画像を取得するステップを含み、
前記所定の骨部領域および前記所定の部材を抽出するステップは、前記機械学習の学習結果に基づいて、前記撮影画像上において、前記所定の骨部領域および前記所定の部材を一体的に抽出するステップを含む、項目1~3のいずれか1項に記載の骨部画像解析方法。 (Item 5)
The step of acquiring the first label image is to obtain the first label image in which a common correct answer value is given to the predetermined bone region and the position corresponding to the first simulated member image on the first label image. Including the steps to get
In the step of extracting the predetermined bone region and the predetermined member, the predetermined bone region and the predetermined member are integrally extracted on the photographed image based on the learning result of the machine learning. The bone image analysis method according to any one of
前記第1ラベル画像を取得するステップは、前記第1ラベル画像上において前記所定の骨部領域および前記第1模擬部材画像に対応する位置に互いに異なる正解値が付与された前記第1ラベル画像を取得するステップを含み、
前記所定の骨部領域および前記所定の部材を抽出するステップは、前記機械学習の学習結果に基づいて、前記撮影画像上において、前記所定の骨部領域および前記所定の部材を個別に抽出するステップを含む、項目1~3のいずれか1項に記載の骨部画像解析方法。 (Item 6)
The step of acquiring the first label image is to obtain the first label image in which different correct answer values are given to the predetermined bone region and the positions corresponding to the first simulated member image on the first label image. Including the steps to get
The step of extracting the predetermined bone region and the predetermined member is a step of individually extracting the predetermined bone region and the predetermined member on the photographed image based on the learning result of the machine learning. The bone image analysis method according to any one of
前記機械学習を実施するステップは、前記第1学習用入力画像の元となった前記骨部領域画像に、前記第1学習用入力画像の前記第1模擬部材画像とは輝度値、形状、位置、および、数のうちの少なくとも1つが異なる、前記所定の部材を模擬した第2模擬部材画像が付加された第2学習用入力画像と、前記第2学習用入力画像において前記所定の骨部領域および前記第2模擬部材画像が表示される位置の第2正解情報を含む第2ラベル画像とを用いて、前記第1学習用入力画像と前記第1ラベル画像とを用いた学習の後に再学習するステップをさらに含み、
前記機械学習の学習結果に基づいて前記所定の骨部領域および前記所定の部材を抽出するステップは、再学習された学習結果に基づいて、前記撮影画像上において、前記所定の骨部領域および前記所定の部材を抽出するステップを含む、項目1~3のいずれか1項に記載の骨部画像解析方法。 (Item 7)
In the step of performing the machine learning, the brightness value, shape, and position of the bone region image that is the source of the first learning input image and the first simulated member image of the first learning input image are obtained. , And a second learning input image to which a second simulated member image simulating the predetermined member, which is different in at least one of the numbers, and the predetermined bone region in the second learning input image. And re-learning after learning using the first learning input image and the first label image using the second label image including the second correct answer information of the position where the second simulated member image is displayed. Including more steps to do
The step of extracting the predetermined bone region and the predetermined member based on the learning result of the machine learning is the step of extracting the predetermined bone region and the predetermined member on the captured image based on the relearned learning result. The bone image analysis method according to any one of
前記複数の骨部領域画像のうちの前記第1模擬部材画像が付加されない前記骨部領域画像において、前記所定の骨部領域が表示される位置の第3正解情報を含む第3ラベル画像を取得するステップをさらに備え、
前記機械学習を実施するステップは、前記第1学習用入力画像と前記第1ラベル画像との組、および、前記第1模擬部材画像が付加されない前記骨部領域画像と前記第3ラベル画像との組を用いて前記機械学習を実施するステップを含む、項目1~3のいずれか1項に記載の骨部画像解析方法。 (Item 8)
In the bone region image to which the first simulated member image is not added among the plurality of bone region images, a third label image including the third correct answer information of the position where the predetermined bone region is displayed is acquired. With more steps to do
The step of performing the machine learning is a set of the first learning input image and the first label image, and the bone region image to which the first simulated member image is not added and the third label image. The bone image analysis method according to any one of
前記第1学習用入力画像を取得するステップは、前記所定の骨部領域が被験者の左半身および右半身の各々に存在する場合に、左右のうちの一方の前記所定の骨部領域が表示された一方側骨部画像に前記第1模擬部材画像が付加された一方側学習用画像と、左右のうちの他方の前記所定の骨部領域が表示された他方側学習用画像が左右反転された他方側骨部反転後画像に前記第1模擬部材画像が付加された他方側学習用画像とを、前記第1学習用入力画像として取得するステップを含む、項目1~3のいずれか1項に記載の骨部画像解析方法。 (Item 9)
In the step of acquiring the first learning input image, when the predetermined bone region exists in each of the left half body and the right half body of the subject, one of the left and right said predetermined bone regions is displayed. The one-sided learning image in which the first simulated member image was added to the one-sided bone image and the other-side learning image in which the predetermined bone region of the other of the left and right was displayed were flipped horizontally. In any one of
前記機械学習は、深層学習を含む、項目1~3のいずれか1項に記載の骨部画像解析方法。 (Item 10)
The bone image analysis method according to any one of
所定の骨部領域が表示された複数の骨部領域画像を取得するステップと、
前記複数の骨部領域画像のうちの一部の前記骨部領域画像に、骨部よりも輝度値が大きい所定の部材を模擬した模擬部材画像を付加することにより、学習用入力画像を取得するステップと、
前記学習用入力画像において前記所定の骨部領域および前記模擬部材画像が表示される位置の正解情報を含むラベル画像を取得するステップと、
前記学習用入力画像と前記ラベル画像とを用いて、X線撮影装置により撮影され、前記所定の骨部領域および前記所定の部材が表示された撮影画像上において、前記所定の骨部領域および前記所定の部材を抽出するための機械学習を実施するステップと、を備える、学習方法。 (Item 11)
A step of acquiring a plurality of bone region images in which a predetermined bone region is displayed, and
A learning input image is acquired by adding a simulated member image simulating a predetermined member having a brightness value larger than that of the bone to the bone region image of a part of the plurality of bone region images. Steps and
A step of acquiring a label image including correct answer information of the predetermined bone region and the position where the simulated member image is displayed in the learning input image, and
Using the learning input image and the label image, the predetermined bone region and the predetermined bone region and the predetermined bone region and the said A learning method comprising a step of performing machine learning for extracting a predetermined member.
20 骨部領域画像
21 右側骨部領域画像(一方側骨部画像)
22 左側骨部領域画像(他方側骨部反転前画像)
23 反転後骨部領域画像(他方側骨部反転後画像)
30 学習用入力画像(第1学習用入力画像)
31 右側学習用入力画像(一方側学習用画像)
32 左側学習用入力画像(他方側学習用画像)
40、70 ラベル画像(第1ラベル画像)
41 ラベル画像(第3ラベル画像)
50 学習用入力画像(第2学習用入力画像)
60 ラベル画像(第2ラベル画像)
100 X線撮影装置
300 部材(所定の部材)
300a 模擬部材画像(第1模擬部材画像)
300b 模擬部材画像(第2模擬部材画像)
400、700、700a、700b 正解情報(第1正解情報)
410 正解情報(第3正解情報)
600 正解情報(第2正解情報)
A 骨部領域(所定の骨部領域)
T 被験者 10 Captured
22 Left bone region image (image before contralateral bone reversal)
23 Post-reversal bone region image (post-reversal image of the other side)
30 Input image for learning (input image for first learning)
31 Right-side learning input image (one-sided learning image)
32 Left side learning input image (other side learning image)
40, 70 label image (first label image)
41 Label image (3rd label image)
50 Input image for learning (input image for second learning)
60 label image (second label image)
100
300a simulated member image (first simulated member image)
300b simulated member image (second simulated member image)
400, 700, 700a, 700b Correct answer information (first correct answer information)
410 Correct answer information (3rd correct answer information)
600 Correct answer information (2nd correct answer information)
A Bone area (predetermined bone area)
T subject
Claims (11)
- 所定の骨部領域が表示された複数の骨部領域画像を取得するステップと、
前記複数の骨部領域画像のうちの一部の前記骨部領域画像に、骨部よりも輝度値が大きい所定の部材を模擬した第1模擬部材画像を付加することにより、第1学習用入力画像を取得するステップと、
前記第1学習用入力画像において前記所定の骨部領域および前記第1模擬部材画像が表示される位置の第1正解情報を含む第1ラベル画像を取得するステップと、
前記第1学習用入力画像と前記第1ラベル画像とを用いて、X線撮影装置により撮影され、前記所定の骨部領域および前記所定の部材が表示された撮影画像上において前記所定の骨部領域および前記所定の部材を抽出するための機械学習を実施するステップと、
前記機械学習の学習結果に基づいて、前記撮影画像上において、前記所定の骨部領域および前記所定の部材を抽出するステップと、を備える、骨部画像解析方法。 A step of acquiring a plurality of bone region images in which a predetermined bone region is displayed, and
Input for first learning by adding a first simulated member image that simulates a predetermined member having a brightness value larger than that of the bone to the bone region image that is a part of the plurality of bone region images. Steps to get the image and
A step of acquiring a first label image including the first correct answer information of the predetermined bone region and the position where the first simulated member image is displayed in the first learning input image.
The predetermined bone portion is photographed by an X-ray apparatus using the first learning input image and the first label image, and the predetermined bone portion is displayed on the captured image in which the predetermined bone region and the predetermined member are displayed. A step of performing machine learning to extract a region and the predetermined member, and
A bone image analysis method comprising a step of extracting the predetermined bone region and the predetermined member on the captured image based on the learning result of the machine learning. - 前記第1学習用入力画像を取得するステップは、前記複数の骨部領域画像のうちの一部の前記骨部領域画像に、少なくとも一部が前記所定の骨部領域の内部に配置された金属を模擬した前記第1模擬部材画像を付加するステップを含む、請求項1に記載の骨部画像解析方法。 The step of acquiring the first learning input image is a metal in which a part of the plurality of bone region images is arranged in the bone region image, and at least a part of the metal is arranged inside the predetermined bone region. The bone image analysis method according to claim 1, further comprising a step of adding the first simulated member image simulating the above.
- 前記金属を模擬した前記第1模擬部材画像を付加するステップは、前記金属の輝度値と略等しい輝度値を有する前記第1模擬部材画像を、前記骨部領域画像に付加するステップを含む、請求項2に記載の骨部画像解析方法。 The step of adding the first simulated member image simulating the metal includes a step of adding the first simulated member image having a brightness value substantially equal to the brightness value of the metal to the bone region image. Item 2. The bone image analysis method according to Item 2.
- 前記第1学習用入力画像を取得するステップは、複数の前記骨部領域画像の各々に前記第1模擬部材画像を付加する場合に、前記第1模擬部材画像が付加される前記複数の骨部領域画像ごとに、前記第1模擬部材画像の輝度値、形状、位置、および、数のうちの少なくとも1つが互いに異なるように、前記複数の骨部領域画像の各々に前記第1模擬部材画像を付加することによって、複数の前記第1学習用入力画像を取得するステップを含む、請求項1~3のいずれか1項に記載の骨部画像解析方法。 In the step of acquiring the first learning input image, when the first simulated member image is added to each of the plurality of bone region image, the plurality of bones to which the first simulated member image is added. For each region image, the first simulated member image is provided in each of the plurality of bone region images so that at least one of the brightness value, shape, position, and number of the first simulated member image is different from each other. The bone image analysis method according to any one of claims 1 to 3, further comprising a step of acquiring a plurality of the first learning input images by adding the image.
- 前記第1ラベル画像を取得するステップは、前記第1ラベル画像上において前記所定の骨部領域および前記第1模擬部材画像に対応する位置に共通の正解値が付与された前記第1ラベル画像を取得するステップを含み、
前記所定の骨部領域および前記所定の部材を抽出するステップは、前記機械学習の学習結果に基づいて、前記撮影画像上において、前記所定の骨部領域および前記所定の部材を一体的に抽出するステップを含む、請求項1~3のいずれか1項に記載の骨部画像解析方法。 The step of acquiring the first label image is to obtain the first label image in which a common correct answer value is given to the predetermined bone region and the position corresponding to the first simulated member image on the first label image. Including the steps to get
In the step of extracting the predetermined bone region and the predetermined member, the predetermined bone region and the predetermined member are integrally extracted on the photographed image based on the learning result of the machine learning. The bone image analysis method according to any one of claims 1 to 3, which comprises a step. - 前記第1ラベル画像を取得するステップは、前記第1ラベル画像上において前記所定の骨部領域および前記第1模擬部材画像に対応する位置に互いに異なる正解値が付与された前記第1ラベル画像を取得するステップを含み、
前記所定の骨部領域および前記所定の部材を抽出するステップは、前記機械学習の学習結果に基づいて、前記撮影画像上において、前記所定の骨部領域および前記所定の部材を個別に抽出するステップを含む、請求項1~3のいずれか1項に記載の骨部画像解析方法。 The step of acquiring the first label image is to obtain the first label image in which different correct answer values are given to the predetermined bone region and the position corresponding to the first simulated member image on the first label image. Including the steps to get
The step of extracting the predetermined bone region and the predetermined member is a step of individually extracting the predetermined bone region and the predetermined member on the photographed image based on the learning result of the machine learning. The bone image analysis method according to any one of claims 1 to 3, which comprises. - 前記機械学習を実施するステップは、前記第1学習用入力画像の元となった前記骨部領域画像に、前記第1学習用入力画像の前記第1模擬部材画像とは輝度値、形状、位置、および、数のうちの少なくとも1つが異なる、前記所定の部材を模擬した第2模擬部材画像が付加された第2学習用入力画像と、前記第2学習用入力画像において前記所定の骨部領域および前記第2模擬部材画像が表示される位置の第2正解情報を含む第2ラベル画像とを用いて、前記第1学習用入力画像と前記第1ラベル画像とを用いた学習の後に再学習するステップをさらに含み、
前記機械学習の学習結果に基づいて前記所定の骨部領域および前記所定の部材を抽出するステップは、再学習された学習結果に基づいて、前記撮影画像上において、前記所定の骨部領域および前記所定の部材を抽出するステップを含む、請求項1~3のいずれか1項に記載の骨部画像解析方法。 In the step of performing the machine learning, the brightness value, shape, and position of the bone region image, which is the source of the first learning input image, are different from those of the first simulated member image of the first learning input image. , And a second learning input image to which a second simulated member image simulating the predetermined member, which is different in at least one of the numbers, and the predetermined bone region in the second learning input image. And re-learning after learning using the first learning input image and the first label image using the second label image including the second correct answer information of the position where the second simulated member image is displayed. Including more steps to do
The step of extracting the predetermined bone region and the predetermined member based on the learning result of the machine learning is the step of extracting the predetermined bone region and the predetermined member on the captured image based on the relearned learning result. The bone image analysis method according to any one of claims 1 to 3, which comprises a step of extracting a predetermined member. - 前記複数の骨部領域画像のうちの前記第1模擬部材画像が付加されない前記骨部領域画像において、前記所定の骨部領域が表示される位置の第3正解情報を含む第3ラベル画像を取得するステップをさらに備え、
前記機械学習を実施するステップは、前記第1学習用入力画像と前記第1ラベル画像との組、および、前記第1模擬部材画像が付加されない前記骨部領域画像と前記第3ラベル画像との組を用いて前記機械学習を実施するステップを含む、請求項1~3のいずれか1項に記載の骨部画像解析方法。 In the bone region image to which the first simulated member image is not added among the plurality of bone region images, a third label image including the third correct answer information of the position where the predetermined bone region is displayed is acquired. With more steps to do
The step of carrying out the machine learning is a set of the first learning input image and the first label image, and the bone region image to which the first simulated member image is not added and the third label image. The bone image analysis method according to any one of claims 1 to 3, which comprises a step of carrying out the machine learning using a set. - 前記第1学習用入力画像を取得するステップは、前記所定の骨部領域が被験者の左半身および右半身の各々に存在する場合に、左右のうちの一方の前記所定の骨部領域が表示された一方側骨部画像に前記第1模擬部材画像が付加された一方側学習用画像と、左右のうちの他方の前記所定の骨部領域が表示された他方側骨部反転前画像が左右反転された他方側骨部反転後画像に前記第1模擬部材画像が付加された他方側学習用画像とを、前記第1学習用入力画像として取得するステップを含む、請求項1~3のいずれか1項に記載の骨部画像解析方法。 In the step of acquiring the first learning input image, when the predetermined bone region exists in each of the left half body and the right half body of the subject, one of the left and right said predetermined bone regions is displayed. The one-side learning image in which the first simulated member image is added to the one-sided bone image and the image before the other-side bone inversion in which the predetermined bone region of the other of the left and right is displayed are left-right inverted. Any of claims 1 to 3, which includes a step of acquiring the other-side learning image to which the first simulated member image is added to the image after reversing the other-side bone portion as the first learning input image. The bone image analysis method according to item 1.
- 前記機械学習は、深層学習を含む、請求項1~3のいずれか1項に記載の骨部画像解析方法。 The bone image analysis method according to any one of claims 1 to 3, wherein the machine learning includes deep learning.
- 所定の骨部領域が表示された複数の骨部領域画像を取得するステップと、
前記複数の骨部領域画像のうちの一部の前記骨部領域画像に、骨部よりも輝度値が大きい所定の部材を模擬した模擬部材画像を付加することにより、学習用入力画像を取得するステップと、
前記学習用入力画像において前記所定の骨部領域および前記模擬部材画像が表示される位置の正解情報を含むラベル画像を取得するステップと、
前記学習用入力画像と前記ラベル画像とを用いて、X線撮影装置により撮影され、前記所定の骨部領域および前記所定の部材が表示された撮影画像上において、前記所定の骨部領域および前記所定の部材を抽出するための機械学習を実施するステップと、を備える、学習方法。 A step of acquiring a plurality of bone region images in which a predetermined bone region is displayed, and
A learning input image is acquired by adding a simulated member image simulating a predetermined member having a brightness value larger than that of the bone to the bone region image of a part of the plurality of bone region images. Steps and
A step of acquiring a label image including correct answer information of the predetermined bone region and the position where the simulated member image is displayed in the learning input image, and
Using the learning input image and the label image, the predetermined bone region and the predetermined bone region and the predetermined bone region and the said A learning method comprising a step of performing machine learning for extracting a predetermined member.
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