Patent Publication Number: US-2023133103-A1

Title: Learning model generation method, image processing apparatus, program, and training data generation method

Description:
CROSS-REFERENCES TO RELATED APPLICATIONS 
     This application is based on and claims priority under 35 U.S.C. § 119 to Japanese Patent Application No. 2021-176886 filed on Oct. 28, 2021, the entire content of which is incorporated herein by reference. 
     TECHNOLOGICAL FIELD 
     The present invention generally relates to a learning model generation method, an image processing apparatus, a program, and a training data generation method. 
     BACKGROUND DISCUSSION 
     A catheter system that acquires an image by inserting an image acquisition catheter into a lumen organ such as a blood vessel has been used (WO 2017/164071 A). An ultrasound diagnostic apparatus that displays a segmentation image in which tissues drawn in an image are classified has been proposed (WO 2020/203873 A). 
     SUMMARY 
     By using a segmentation image created based on an image captured using a catheter system, for example, automatic measurement of the area, the volume, or the like, and display of a three-dimensional image are enabled. 
     However, in a known segmentation approach, there is a case where it is difficult to accurately classify a region drawn thinly in an image. 
     In one aspect, a learning model generation method and the like disclosed here generate a learning model configured to accurately classify a thinly drawn region. 
     A learning model generation method includes: acquiring training data from a training database that records a plurality of sets of a tomographic image acquired using a tomographic image acquisition probe, and correct answer classification data in which the tomographic image is classified into a plurality of regions including a living tissue region and a non-living tissue region, in association with each other; acquiring thin-walled part data relating to a thin-walled part thinner than a predetermined threshold value, for a predetermined region in the correct answer classification data; and performing a parameter adjustment process for a learning model, based on the training data and the thin-walled part data. 
     In one aspect, a learning model generation method and the like that generates a learning model configured to accurately classify a thinly drawn region can be provided. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is an explanatory diagram explaining a generation method for a classification model; 
         FIG.  2    is an explanatory diagram explaining a configuration of an information processing apparatus; 
         FIG.  3    is an explanatory diagram explaining a record layout of a classification training database (DB); 
         FIG.  4    is an explanatory diagram explaining thin-walled part data; 
         FIG.  5    is an explanatory diagram explaining the thin-walled part data; 
         FIG.  6    is an explanatory diagram explaining the thin-walled part data; 
         FIG.  7    is an explanatory diagram explaining difference data; 
         FIG.  8    is an explanatory diagram explaining weighted difference data; 
         FIG.  9    is a flowchart explaining a processing flow of a program; 
         FIG.  10    is a flowchart explaining a processing flow of a subroutine for thin-walled part data generation; 
         FIG.  11    is an explanatory diagram explaining a modification of the difference data; 
         FIG.  12    is an explanatory diagram explaining a modification of the difference data; 
         FIG.  13    is an explanatory diagram explaining a modification of the weighted difference data; 
         FIG.  14    is an explanatory diagram explaining a modification of the weighted difference data; 
         FIG.  15    is an explanatory diagram explaining a modification of the thin-walled part data; 
         FIG.  16    is an explanatory diagram explaining a thin-walled part extraction model; 
         FIG.  17    is an explanatory diagram explaining a record layout of a thin-walled part training DB; 
         FIG.  18    is an explanatory diagram explaining a generation method for the classification model according to a modification 1-8; 
         FIG.  19    is an explanatory diagram explaining a generation method for a classification model according to a second embodiment; 
         FIG.  20    is an explanatory diagram explaining the generation method for the classification model according to the second embodiment; 
         FIG.  21    is an explanatory diagram explaining the generation method for the classification model according to the second embodiment; 
         FIG.  22    is an explanatory diagram explaining the generation method for the classification model according to the second embodiment; 
         FIG.  23    is an explanatory diagram explaining weighted correct answer classification data according to a modification 2-1; 
         FIG.  24    is an explanatory diagram explaining a loss value according to a third embodiment; 
         FIG.  25    is an explanatory diagram explaining the loss value according to the third embodiment; 
         FIG.  26    is a flowchart explaining a processing flow of a program according to the third embodiment; 
         FIG.  27    is a flowchart explaining a processing flow of a subroutine for loss value calculation; 
         FIG.  28    is an explanatory diagram explaining difference data according to a fourth embodiment; 
         FIG.  29    is a flowchart explaining a processing flow of a program according to a fifth embodiment; 
         FIG.  30    is an explanatory diagram explaining a configuration of a catheter system according to a sixth embodiment; 
         FIG.  31    is a flowchart explaining a processing flow of a program according to the sixth embodiment; 
         FIG.  32    is an explanatory diagram explaining a configuration of an information processing apparatus according to a seventh embodiment; 
         FIG.  33    is a functional block diagram of an information processing apparatus according to an eighth embodiment; and 
         FIG.  34    is a functional block diagram of an image processing apparatus according to a ninth embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     First Embodiment 
       FIG.  1    is an explanatory diagram explaining a generation method for a classification model  31 . A large number of pieces of classification training data in which a tomographic image  58  and correct answer classification data  57  are combined as a set are recorded in a classification training database (DB)  41  (see  FIG.  2   ). In the present embodiment, a case where the tomographic image  58  is an ultrasound tomographic image captured using an image acquisition catheter  28  (see  FIG.  30   ) for intravascular ultrasound (IVUS) will be described as an example. The image acquisition catheter  28  is an example of a tomographic image acquisition probe that acquires the tomographic image  58  of the body of a patient. 
     The tomographic image  58  may be a tomographic image  58  by optical coherence tomography (OCT) using near-infrared light. The tomographic image  58  may be an ultrasound tomographic image acquired using the linear scanning or sector scanning image acquisition catheter  28 . The tomographic image  58  may be an ultrasound tomographic image acquired using a transesophageal echocardiography (TEE) probe. The tomographic image  58  may be an ultrasound tomographic image acquired using an extracorporeal ultrasound probe that is applied to the body surface of the patient. 
       FIG.  1    illustrates the tomographic image  58  in a so-called RT format formed by arranging scanning line data in parallel in the order of the scanning angle. The left end of the tomographic image  58  represents the image acquisition catheter  28 . A horizontal direction of the tomographic image  58  corresponds to the distance to the image acquisition catheter  28 , and a vertical direction of the tomographic image  58  corresponds to the scanning angle. 
     The correct answer classification data  57  is data obtained by classifying each pixel included in the tomographic image  58  into a living tissue region  566 , a lumen region  563 , and an extraluminal region  567 . The lumen region  563  is a region circumferentially surrounded by the living tissue region  566 . The lumen region  563  is classified into a first lumen region  561  into which the image acquisition catheter  28  is inserted and a second lumen region  562  into which the image acquisition catheter  28  is not inserted. In the following description, each piece of data constituting the correct answer classification data  57  is also described as a “pixel” similarly to the data included in the tomographic image  58 . 
     Each pixel is associated with a label indicating the region into which the pixel is classified. In  FIG.  1   , a portion associated with the label of the living tissue region  566  is indicated by grid hatching, a portion associated with the label of the first lumen region  561  is indicated by no hatching, a portion associated with the label of the second lumen region  562  is indicated by left-downward hatching, and a portion associated with the label of the extraluminal region  567  is indicated by right-downward hatching. Note that the labels may be associated with each small region obtained by collecting a plurality of pixels included in the tomographic image  58 . 
     A case where the image acquisition catheter  28  is inserted into a circulatory organ such as a blood vessel or a heart will be specifically described as an example. The living tissue region  566  corresponds to a lumen organ wall, such as a blood vessel wall or a heart wall. The first lumen region  561  is a region inside the lumen organ into which the image acquisition catheter  28  is inserted. That is, the first lumen region  561  is a region filled with blood. 
     The second lumen region  562  is a region inside another lumen organ located in the vicinity of the blood vessel or the like into which the image acquisition catheter  28  is inserted. For example, the second lumen region  562  is a region inside a blood vessel branched from the blood vessel into which the image acquisition catheter  28  is inserted or a region inside another blood vessel close to the blood vessel into which the image acquisition catheter  28  is inserted. There is also a case where the second lumen region  562  is a region inside a lumen organ other than the circulatory organ, such as a bile duct, a pancreatic duct, a ureter, or a urethra as an example. 
     The extraluminal region  567  is a region outside the living tissue region  566 . When the living tissue region  566  on a distal side of the image acquisition catheter  28  is not accommodated within the display range of the tomographic image  58  even in a region inside an atrium, a ventricle, a thick blood vessel, or the like, the living tissue region  566  is classified into the extraluminal region  567 . 
     Although not illustrated, the correct answer classification data  57  may include labels corresponding to a variety of regions such as an instrument region in which the image acquisition catheter  28  and a guide wire and the like inserted together with the image acquisition catheter  28  are drawn, and a lesion region in which a lesion such as calcification is drawn, as an example. 
     The correct answer classification data  57  may be data in which both of the first lumen region  561  and the second lumen region  562  are classified into the lumen region  563  without being distinguished from each other. The correct answer classification data  57  may be data classified into two types of regions, namely, the living tissue region  566  and a non-living tissue region. 
     The correct answer classification data  57  is created by an expert such as a medical doctor or a clinical examination technician who is proficient in interpreting the tomographic image  58 , or a trained operator and is recorded in the classification training DB  41  in association with the tomographic image  58 . 
     Thin-walled part data  59  obtained by extracting a thin-walled part region  569  thinner than a predetermined threshold value for a specified region is generated from the correct answer classification data  57 .  FIG.  1    illustrates an example of a case where the thin-walled part regions  569  are portions where the living tissue region  566  is thinner than a predetermined threshold value. Details of the extraction method for the thin-walled part region  569  will be described later. 
     Machine learning of the classification model  31  that outputs output classification data  51  when the tomographic image  58  is input is performed using the classification training DB  41 . Here, the classification model  31  is, for example, a model having a U-Net structure that implements semantic segmentation. The classification model  31  is an example of a learning model of the present embodiment. 
     The U-Net structure includes a multi-layer encoder layer and a multi-layer decoder layer connected behind the encoder layer. Each encoder layer includes a pooling layer and a convolution layer. The output classification data  51  in which each pixel constituting the input tomographic image  58  is labeled is generated by semantic segmentation. In the following description, each piece of data constituting the output classification data  51  is also described as a “pixel” similarly to the data included in the tomographic image  58 . Note that the classification model  31  may be a mask region-based convolutional neural network (Mask R-CNN) model or any other model that implements segmentation of an image. 
     An outline of a machine learning method will be described. One set of classification training data is acquired from the classification training DB  41 . The tomographic image  58  is input to the classification model  31  in the middle of learning, and the output classification data  51  is output. Difference data  55  is generated based on the comparison between the output classification data  51  and the correct answer classification data  57 . 
     The difference data  55  is data relating to the difference between the label of each pixel constituting the correct answer classification data  57  and the label of the corresponding pixel in the output classification data  51 . The output classification data  51 , the correct answer classification data  57 , and the difference data  55  have the same number of pieces of data. In the following description, each piece of data constituting the difference data  55  is also described as a pixel. 
     A loss value  551 , which is a calculated value relating to the difference between the correct answer classification data  57  and the output classification data  51 , is defined based on the difference data  55  weighted using the thin-walled part data  59 . Parameter adjustment for the classification model  31  is performed using, for example, the back propagation method such that the loss value  551  approaches a predetermined value. The predetermined value is a small value such as “0” or “0.1”. 
     Details of the creation of the difference data  55 , the weighting by the thin-walled part data  59 , and the calculation of the loss value  551  will be described later. By machine learning in which parameter adjustment is repeated using a large number of pieces of the classification training data, the classification model  31  configured to accurately classify even a portion corresponding to the thin-walled part region  569  is generated. 
       FIG.  2    is an explanatory diagram explaining a configuration of an information processing apparatus  20 . The information processing apparatus  20  includes a control unit  21 , a main storage device  22 , an auxiliary storage device  23 , a communication unit  24 , a display unit  25 , an input unit  26 , and a bus. The control unit  21  is an arithmetic control device that executes a program of the present embodiment. For the control unit  21 , one or a plurality of central processing units (CPUs) or graphics processing units (GPUs), a multi-core CPU, or the like is used. The control unit  21  is connected to each hardware unit constituting the information processing apparatus  20  via the bus. 
     The main storage device  22  is a storage device such as a static random access memory (SRAM), a dynamic random access memory (DRAM), or a flash memory. In the main storage device  22 , information involved in the middle of the process performed by the control unit  21  and the program being executed by the control unit  21  are temporarily saved. 
     The auxiliary storage device  23  is a storage device such as an SRAM, a flash memory, a hard disk, or a magnetic tape. In the auxiliary storage device  23 , the classification model  31 , the classification training DB  41 , a program to be executed by the control unit  21 , and various sorts of data involved in executing the program are saved. The classification model  31  and the classification training DB  41  may be stored in an external mass storage device or the like connected to the information processing apparatus  20 . 
     The communication unit  24  is an interface that performs communication between the information processing apparatus  20  and a network. For example, the display unit  25  is a liquid crystal display panel, an organic electro luminescence (EL) panel, or the like. For example, the input unit  26  is a keyboard, a mouse, or the like. The input unit  26  may be stacked on the display unit  25  to constitute a touch panel. The display unit  25  may be a display device connected to the information processing apparatus  20 . The information processing apparatus  20  may not include the display unit  25  or the input unit  26 . 
     The information processing apparatus  20  is a general-purpose personal computer, a tablet, a large computing machine, or a virtual machine that works on a large computing machine. The information processing apparatus  20  may be constituted by a plurality of personal computers that perform distributed processing, or hardware such as a large computing machine. The information processing apparatus  20  may be constituted by a cloud computing system or a quantum computer. 
       FIG.  3    is an explanatory diagram explaining a record layout of the classification training DB  41 . The classification training DB  41  is a database in which a large number of sets of the tomographic image  58  and the correct answer classification data  57  are recorded in association with each other. 
     The classification training DB  41  includes a tomographic image field and a correct answer classification data field. Each of the tomographic image field and the correct answer classification data field has two subfields, namely, an RT format field and an XY format field. 
     The RT format field of the tomographic image field records the tomographic image  58  in the RT format formed by arranging scanning line data in parallel in the order of the scanning angle. The XY format field of the tomographic image field records the tomographic image  58  in the XY format generated by conducting coordinate transformation on the tomographic image  58  in the RT format. 
     The RT format field of the correct answer classification data field records the correct answer classification data  57  in the RT format in which the tomographic image  58  in the RT format is classified into a plurality of regions. The XY format field of the correct answer classification data field records the correct answer classification data  57  in the XY format in which the tomographic image  58  in the XY format is classified into a plurality of regions. 
     Note that the tomographic image  58  in the XY format may be generated by coordinate transformation from the tomographic image  58  in the RT format if applicable, instead of being recorded in the classification training DB  41 . Only one of the correct answer classification data  57  in the RT format and the correct answer classification data  57  in the XY format may be recorded in the classification training DB  41 , and the other may be generated by coordinate transformation if applicable. The classification training DB  41  has one record for one set of classification training data. The classification training DB  41  is an example of a training database of the present embodiment. 
       FIGS.  4  to  6    are explanatory diagrams explaining the thin-walled part data  59 . In the following description, a case where the control unit  21  extracts the thin-walled part region  569  in which the living tissue region  566  is thin, from the correct answer classification data  57  will be described as an example. 
     The control unit  21  extracts the living tissue region  566  from the correct answer classification data  57 . A state in which the living tissue region  566  is extracted is illustrated in the upper right of  FIG.  4   . The control unit  21  extracts a boundary line  53  between the living tissue region  566  and regions other than the living tissue region  566 , using a known edge extraction algorithm. A state in which the boundary line  53  is extracted is illustrated in the center on the right side of  FIG.  4   . 
       FIG.  5    illustrates an enlarged view of the V portion in  FIG.  4   . The control unit  21  generates a measurement line  539  that passes only through the living tissue region  566  from an optional point on the boundary line  53  and reaches another point on the boundary line  53 . In  FIG.  5   , a case where the control unit  21  generates the measurement lines  539  that pass only through the living tissue region  566  from the point A 1  on the boundary line  53  and reach other points on the boundary line  53  will be described as an example. 
     The control unit  21  calculates the length of each measurement line  539  and selects the shortest measurement line  539 . In  FIG.  5   , the measurement line  539  connecting the points A 1  and A 2 , which has been selected by the control unit  21 , is indicated by a solid line, and the measurement lines  539  not selected by the control unit  21  are indicated by broken lines. 
     The control unit  21  determines whether the selected measurement line  539  is shorter than a predetermined threshold value. When the selected measurement line  539  is not shorter than the predetermined threshold value, the control unit  21  does not perform the process related to the selected measurement line  539 . If the selected measurement line  539  is shorter than the predetermined threshold value, the living tissue region  566  is thinner than a predetermined threshold value in the portion where the measurement line  539  is generated. In the following description, a case where the measurement line  539  connecting the points A 1  and A 2  is shorter than the threshold value will be described as an example. 
       FIG.  6    schematically illustrates an enlarged view of the thin-walled part data  59  corresponding to the VI portion in  FIG.  5   . The thin-walled part data  59  has the same number of pixels as the correct answer classification data  57 . Each frame in  FIG.  6    indicates one pixel. The control unit  21  records a thin-walled part flag in the pixel through which the measurement line  539  connecting the points A 1  and A 2  passes. In the example illustrated in  FIG.  6   , the thin-walled part flag is “1”. Although not illustrated, a predetermined flag such as “0” is recorded in the pixel in which the thin-walled part flag is not recorded. The control unit  21  records the thin-walled part flag in the pixel through which the measurement line  539  passes, for all the measurement lines  539  satisfying the condition, by the same procedure. 
     Note that the control unit  21  may receive an input by a user regarding the threshold value for the length of the measurement line  539 . The user inputs an appropriate threshold value used for determining whether the region is the thin-walled part region  569 , based on the physique of the patient, the disease state, and the like. 
     Returning to  FIG.  4   , the description will be continued. In  FIG.  6   , the portions where the thin-walled part flags are recorded are the thin-walled part regions  569  illustrated in the lower right of  FIG.  4   . As described with reference to  FIGS.  4  to  6   , by extracting the thin-walled part region  569  in the XY format and displaying the extracted thin-walled part region  569  on the display unit  25 , the user is allowed to confirm whether the control unit  21  has accurately extracted an anatomically thin portion such as a fossa ovalis. 
     As illustrated in the lower left of  FIG.  4   , the thin-walled part data  59  in the XY format can be converted into the RT format by coordinate transformation. Note that the control unit  21  may extract the thin-walled part region  569  from the correct answer classification data  57  in the RT format. 
       FIG.  7    is an explanatory diagram explaining the difference data  55 . The upper left part of  FIG.  7    schematically illustrates nine pixels of the correct answer classification data  57 . The upper right part of  FIG.  7    schematically illustrates nine pixels of the output classification data  51 . The lower part of  FIG.  7    schematically illustrates nine pixels of the difference data  55 . Each group of the nine pixels illustrated in  FIG.  7    indicates a group of pixels located at the corresponding place. For the correct answer classification data  57  and the difference data  55 , the pixels in the center and the lower center on which the rounded rectangles are displayed are pixels corresponding to the thin-walled part region  569 . 
     Each pixel of the correct answer classification data  57  and the output classification data  51  records a probability for a label into which the relevant pixel is classified. “1” means the label of the first lumen region  561 , “2” means the label of the second lumen region  562 , and “3” means the label of the living tissue region  566 . 
     Note that the probabilities that the pixel has four or more types of labels or the probabilities that the pixel has two or less types of labels may be recorded for each pixel. For example, in a case where only the classification as to whether or not the pixel falls under the living tissue region  566  is performed, a probability that the pixel has a label indicating “YES” and a probability that the pixel has a label indicating “NO”, or a probability that the pixel has either a label of “YES” or a label of “NO” is recorded for each pixel. 
     Each pixel of the correct answer classification data  57  is classified into any one of the first lumen region  561 , the second lumen region  562 , and the living tissue region  566  by an expert. Therefore, the probability for any one of the labels is 100%, and the probabilities for the other labels are 0%. In both of the correct answer classification data  57  and the output classification data  51 , the sum of the probabilities for each label is 100% in every pixel. 
     In the following description, the label classified by an expert for every pixel will be sometimes described as a correct answer label, and other labels will be sometimes described as incorrect answer labels. For example, in the center pixels of the correct answer classification data  57 , the output classification data  51 , and the difference data  55  in  FIG.  7   , “1” and “2” represent the incorrect answer labels, and “3” represents the correct answer label. In  FIG.  7   , the correct answer label is indicated in bold, and the incorrect answer label is indicated in oblique. 
     Each pixel of the output classification data  51  records the probability of falling under the first lumen region  561 , the probability of falling under the second lumen region  562 , and the probability of falling under the living tissue region  566 . For example, in the output classification data  51  illustrated in  FIG.  7   , the probability that the pixel in the upper right falls under the first lumen region  561  is 80%, the probability that the pixel falls under the second lumen region  562  is 15%, and the probability that the pixel falls under the living tissue region  566  is 5%. In every pixel, the sum of the probabilities for each label is 100%. The control unit  21  inputs the tomographic image  58  to the classification model  31  and acquires the output classification data  51  output from the classification model  31 . 
     The difference data  55  records losses relating to each label of each pixel. In the following description, each piece of data constituting the difference data  55  is also described as a “pixel” similarly to the data included in the tomographic image  58 . The control unit  21  calculates losses relating to each label of each pixel constituting the difference data  55 , based on the output classification data  51 , the correct answer classification data  57 , and formula (1) to generate the difference data  55 . 
     
       
         
           
             
               
                 
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     Eij indicates the loss relating to the j-th label of the i-th pixel. 
     Ln(x) indicates a natural logarithm of x. 
     Qij indicates a probability that the i-th pixel has the j-th label in the output classification data. 
     Note that Qij is a positive value equal to or smaller than one. Formula (1) is an example of a computation formula when the difference data  55  is generated. The calculation formula for losses relating to each label of each pixel is not limited to formula (1). Modifications of the difference data  55  will be described later. 
       FIG.  8    is an explanatory diagram explaining weighted difference data  65 . The weighted difference data  65  is data indicating losses calculated by weighting each pixel of the difference data  55  based on the thin-walled part data  59 . In the following description, each piece of data constituting the weighted difference data  65  is also described as a “pixel” similarly to the data included in the tomographic image  58 . 
     The control unit  21  calculates losses relating to each pixel constituting the weighted difference data  65 , based on, for example, formula (2). The loss relating to the thin-walled part region  569  is weighted according to formula (2). 
     
       
         
           
             
               
                 
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     Fi indicates the loss of the i-th pixel. 
     Gi indicates a weight relating to the thin-walled part region. 
     When the i-th pixel falls under the thin-walled part region, Gi=m holds. 
     When the i-th pixel does not fall under the thin-walled part region, Gi=1 holds. 
     m indicates a thin-walled part coefficient that is a constant greater than one. 
     u denotes the number of regions into which the pixel is classified. 
     Formula (2) indicates that the loss of each pixel is defined such that the loss of the pixel classified into the thin-walled part region  569  has a weight of m times the loss of the pixel classified into a region other than the thin-walled part region  569 . The thin-walled part coefficient m is, for example, three. 
     The control unit  21  may define the thin-walled part coefficient m in formula (2) based on the thickness of the thin-walled part region  569 . For example, the control unit  21  makes the thin-walled part coefficient m for the thin-walled part region  569  thinner than the threshold value greater than the thin-walled part coefficient m for the thin-walled part region  569  having a thickness equal to or greater than the threshold value. The thin-walled part coefficient m may be defined by, for example, a function for the thickness of the thin-walled part region  569 . 
     Note that the weighted loss calculation method is not limited to formula (2). Some modifications will be described later. 
     The control unit  21  calculates the loss value  551 , based on the weighted difference data  65 . The loss value  551  is a representative value of losses of the respective pixels constituting the weighted difference data  65 . When the arithmetic mean value is used as the representative value, the control unit  21  calculates the loss value  551  based on formula (3). 
     
       
         
           
             
               
                 
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     C indicates the number of pixels. 
     The representative value used for the loss value  551  may be any representative value such as a geometric mean value, a harmonic mean value, or a sum of squares as an example. 
     The control unit  21  may define the loss value  551  based on the loss Fi of one or a plurality of pixels. For example, the control unit  21  may calculate the loss value  551  based on a pixel whose distance from the image acquisition catheter  28  is within a predetermined range. 
     The control unit  21  adjusts the parameters of the classification model  31  using, for example, the back propagation method such that the loss value  551  approaches a predetermined value. By repeating parameter adjustment for the classification model  31  using a large number of pieces of the classification training data, the control unit  21  performs machine learning of the classification model  31  such that the classification model  31  outputs the appropriate output classification data  51  when the tomographic image  58  is input. 
       FIG.  9    is a flowchart explaining a processing flow of a program. The control unit  21  acquires one set of classification training data from the classification training DB  41  (step S 501 ). In step S 501 , the control unit  21  implements the function of a training data acquisition unit of the present embodiments. The control unit  21  activates a subroutine for thin-walled part data generation (step S 502 ). The subroutine for thin-walled part data generation is a subroutine that generates the thin-walled part data  59  based on the correct answer classification data  57 . A processing flow of the subroutine for thin-walled part data generation will be described later. In step S 502 , the control unit  21  implements the function of a thin-walled part data acquisition unit of the present embodiments. 
     The control unit  21  inputs the tomographic image  58  to the classification model  31  being trained and acquires the output classification data  51  (step S 503 ). Using the correct answer classification data  57  and the output classification data  51 , the control unit  21  calculates the difference data  55  based on, for example, formula (1) (step S 504 ). The control unit  21  calculates the weighted difference data  65  based on, for example, formula (2) (step S 505 ). 
     The control unit  21  calculates the loss value  551  based on, for example, formula (3) (step S 506 ). The control unit  21  performs parameter adjustment for the classification model  31  using, for example, the back propagation method such that the loss value  551  approaches a predetermined value (step S 507 ). In step S 507 , the control unit  21  implements the function of a parameter adjustment unit of the present embodiments. 
     The control unit  21  determines whether to end the process (step S 508 ). For example, when a predetermined number of pieces of the classification training data have been learned, the control unit  21  determines to end the process. For example, when the loss value  551  or the amount of adjustment of the parameters falls below a predetermined threshold value, the control unit  21  may determine to end the process. 
     When determining not to end the process (NO in step S 508 ), the control unit  21  returns to step S 501 . When determining to end the process (YES in step S 508 ), the control unit  21  records the adjusted parameters of the classification model  31  in the auxiliary storage device  23  (step S 509 ). Thereafter, the control unit  21  ends the process. As described above, the generation of the classification model  31  ends. 
       FIG.  10    is a flowchart explaining a processing flow of the subroutine for thin-walled part data generation. The subroutine for thin-walled part data generation is a subroutine that generates the thin-walled part data  59  based on the correct answer classification data  57 . The control unit  21  executes the process described with reference to  FIGS.  4  to  6    by the subroutine for thin-walled part data generation. 
     The control unit  21  initializes the thin-walled part data  59  (step S 521 ). Specifically, the control unit  21  sets all the pixels of the thin-walled part data  59  having the same number of pixels as the correct answer classification data  57  to a predetermined initial value. In the following description, a case where the predetermined initial value is “0” will be described as an example. 
     The control unit  21  creates a copy of the correct answer classification data  57 . The control unit  21  performs the process described below on the copy of the correct answer classification data  57 . Note that, in the description below, the copy of the correct answer classification data  57  will be sometimes simply described as the correct answer classification data  57 . 
     The control unit  21  extracts a first label region in which a first label is recorded in the pixel, from the correct answer classification data  57  (step S 522 ). Specifically, the control unit  21  records “1” in the pixel in which the label of the first label region is recorded and records “0” in the pixel in which the label of a region other than the first label region is recorded. In the example described with reference to  FIG.  1   , the first label region is the living tissue region  566 . In the upper right diagram of  FIG.  4   , the pixels in which “1” is recorded are indicated by grid hatching. Note that the first label region is not limited to the living tissue region  566 . For example, the first lumen region  561  or the second lumen region  562  may be the first label region. 
     The control unit  21  extracts the boundary line  53  of the first label region, using a known edge extraction algorithm (step S 523 ). The center diagram on the right side of  FIG.  4    illustrates a state in which the boundary line  53  is extracted. The control unit  21  selects a start point from among the pixels on the boundary line  53  (step S 524 ). The control unit  21  creates a plurality of measurement lines  539  that go through only the first label region from the start point and reach other pixels on the boundary line  53  (step S 525 ). 
     The control unit  21  selects the shortest measurement line  539  from among the plurality of measurement lines  539  created in step S 525  (step S 526 ). The control unit  21  determines whether the measurement line  539  selected in step S 526  is shorter than the threshold value (step S 527 ). The threshold value is, for example, five millimeters. 
     When determining that the measurement line  539  is shorter than the threshold value (YES in step S 527 ), the control unit  21  records the thin-walled part flag in the pixels on the thin-walled part data  59  corresponding to the pixels through which the measurement line  539  passes, as described with reference to  FIG.  6    (step S 528 ). 
     When determining that the measurement line  539  is not shorter than the threshold value (NO in step S 527 ), or after the end of step S 528 , the control unit  21  determines whether to end the creation of the measurement line  539  (step S 529 ). For example, when all the pixels on the boundary line  53  have been selected as the start point in step S 524 , the control unit  21  chooses to end the process. The control unit  21  may choose to end the process when all the pixels selected at predetermined intervals on the boundary line  53  have been selected as the start point in step S 524 . 
     When determining not to end the process (NO in step S 529 ), the control unit  21  returns to step S 524 . When determining to end the process (YES in step S 529 ), the control unit  21  records the created thin-walled part data  59  in the auxiliary storage device  23  or the main storage device  22  (step S 530 ). Thereafter, the control unit  21  ends the process. 
     According to the present embodiment, a learning model generation method that generates the classification model  31  configured to accurately classify a thinly drawn region in the tomographic image  58  can be provided. The classification model  31  generated according to the present embodiment enables to appropriately extract a living tissue having a small wall thickness, such as the fossa ovalis, the tricuspid valve, and the mitral valve, which are sites punctured with a puncture needle when atrial septal puncture is performed. 
     After machine learning is first performed by a normal approach that does not use the thin-walled part data  59 , additional learning of the classification model  31  may be performed by the approach of the present embodiment. After machine learning is performed using training data different from the tomographic image  58  recorded in the classification training DB  41 , the transfer learning may be performed by the approach of the present embodiment. This allows to generate the classification model  31  having good performance in a shorter time than a case where the thin-walled part data  59  is used from an initial stage of machine learning. 
     [Modification 1-1] 
     The present modification illustrates a modification of the calculation method for the difference data  55 . In the present modification, the loss is weighted based on the distance between the i-th pixel and the image acquisition catheter  28 . For example, the control unit  21  calculates losses relating to each label of each pixel, using formula (4) instead of formula (1). 
     
       
         
           
             
               
                 
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                   ( 
                   4 
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     Ri indicates the distance between the i-th pixel and the image acquisition catheter. 
     By using Eij calculated from formula (4) and formulas (2) and (3), the loss value  551  is defined such that the loss in the pixel falling under the thin-walled part region  569  has a greater influence than the loss in the pixel not falling under the thin-walled part region  569 , and the loss in the pixel located at a place near the image acquisition catheter  28  has a greater influence than the loss in the pixel located at a place far from the image acquisition catheter  28 . 
     Note that the weighting based on the distance between the i-th pixel and the image acquisition catheter  28  is not limited to formula (4). The denominator of formula (4) may be, for example, the square root of the distance Ri, the square of the distance Ri, or the like. 
     [Modification 1-2] 
       FIG.  11    is an explanatory diagram explaining a modification of the difference data  55 . In the present modification, losses relating to each label of each pixel are calculated based on the absolute value of the difference between the correct answer classification data  57  and the output classification data  51 . 
       FIG.  11    illustrates an example in which the absolute value of the difference between the correct answer classification data  57  and the output classification data  51  is recorded in each pixel of the difference data  55  for the probabilities that the pixel falls under each region. In the difference data  55  illustrated in  FIG.  11   , it is recorded that the upper right pixel has a difference of 20% for the first lumen region  561 , a difference of 10% for the second lumen region  562 , and a difference of 10% for the living tissue region  566 . 
     Note that the square of the difference between the correct answer classification data  57  and the output classification data  51  may be recorded in each pixel of the difference data  55 . When the square is used, the absolute value does not have to be calculated. 
     [Modification 1-3] 
       FIG.  12    is an explanatory diagram explaining a modification of the difference data  55 . In the present modification, for the correct answer classification data  57  and the output classification data  51 , simple numerical values are used instead of using the probabilities that the pixel falls under each region. 
     The control unit  21  multiplies each piece of data included in the correct answer classification data  57  by a constant to calculate second correct answer classification data  572 . The control unit  21  multiplies each piece of data included in the output classification data  51  by a constant to calculate second output classification data  512 . 
       FIG.  12    illustrates an example of a case where both of the correct answer classification data  57  and the output classification data  51  are multiplied by three to calculate the second correct answer classification data  572  and the second output classification data  512 . In the second correct answer classification data  572  and the second output classification data  512 , the sum of the values corresponding to each region is not one in every pixel, and thus the values are not numerical values indicating the probabilities that the pixel falls under each region. 
     In the example illustrated in  FIG.  12   , the absolute value of the difference between the second correct answer classification data  572  and the second output classification data  512  is recorded in each pixel of the difference data  55  for each region. Instead of formula (1), this absolute value is used as Eij indicating the loss relating to the j-th label of the i-th pixel. Then, the loss value  551  is calculated using Eij calculated in this manner and formulas (2) and (3). The control unit  21  performs parameter adjustment for the classification model  31  using, for example, the back propagation method such that the loss value  551  approaches a predetermined value. The predetermined value is a small value such as “0” or “0.1”. This adjusts the parameters of the classification model  31  such that the second output classification data  512  approaches the second correct answer classification data. 
     Note that the constant at the time of calculating the second correct answer classification data  572  and the constant at the time of calculating the second output classification data  512  may have different values. The classification model  31  may be configured to output the second output classification data  512  multiplied by a constant, instead of the output classification data  51 . The correct answer classification data  57  may be recorded in the classification training DB  41  in a multiplied state by a constant. 
     The constant at the time of calculating the second correct answer classification data  572  and the constant at the time of calculating the second output classification data  512  may have different values for each pixel. Specifically, the constant is set such that a pixel having a closer distance from the image acquisition catheter  28  has a greater value. This defines the loss value  551  such that the loss at a place close to the image acquisition catheter  28  has a greater influence than the loss at a place far from the image acquisition catheter  28 . 
     In addition, as in the second correct answer classification data  572 , learning may be performed directly using correct answer classification data  57  created with a predetermined value such as “3” as the correct answer label and “O” as the incorrect answer label. In this case, the classification model  31  outputs the value of the label of each region as the output classification data  51  for each pixel. Then, instead of the difference data  55  illustrated in  FIG.  12   , difference data is calculated as the absolute value of the difference between the correct answer classification data  57  and the output classification data  51  for each region, and the loss value  551  is calculated and parameter adjustment is performed on the basis of this difference data. 
     This adjusts the parameters of the classification model  31  such that the output classification data  51  approaches the correct answer classification data  57 . Note that, when the classification model  31  outputs the output classification data  51 , machine learning of the classification model  31  can be efficiently carried out by setting the lower limit value for the label value of each region to “0” and matching the sum of the label values of the respective regions in one pixel with a predetermined value of the correct answer label or setting the upper limit value of the region label in one pixel to a predetermined value of the correct answer label. 
     [Modification 1-4] 
       FIG.  13    is an explanatory diagram explaining a modification of the weighted difference data  65 . In the present modification, the weighted difference data  65  is calculated based on only the data relating to the correct answer label without using the data relating to the incorrect answer label. The control unit  21  calculates the loss of each pixel based on, for example, formula (5) instead of formula (2). 
       [Math. 5] 
         Fi=E   ik   G   i   (5)
 
     k indicates the number given to the correct answer region in the i-th pixel. 
     In the present modification, the loss Fi of the i-th pixel that is not classified into the thin-walled part region  569  is the loss of the correct answer label of the i-th pixel in the difference data  55 . In the present modification, since the difference data  55  does not have to be calculated for the incorrect answer label, the control unit  21  is allowed to calculate the weighted difference data  65  with a small computation amount. 
     [Modification 1-5] 
       FIG.  14    is an explanatory diagram explaining a modification of the weighted difference data  65 . In the present modification, the weighted difference data  65  is calculated based on only the data relating to the incorrect answer label without using the data relating to the correct answer label. The control unit  21  calculates the loss of each pixel based on, for example, formula (6) instead of formula (2). 
     
       
         
           
             
               
                 
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     Hj indicates whether the j-th label is the correct answer label or the incorrect answer label. 
     When the j-th label is the correct answer label, Hj=0 holds. 
     When the j-th label is the incorrect answer label, Hj=1 holds. 
     [Modification 1-6] 
       FIG.  15    is an explanatory diagram explaining a modification of the thin-walled part data  59 . In  FIG.  15   , each diagram is illustrated in the RT format. First thin-walled part data  591  is data obtained by extracting the living tissue region  566  from the correct answer classification data  57  and then extracting the thin-walled part region  569 . Second thin-walled part data  592  is data obtained by extracting the second lumen region  562  from the correct answer classification data  57  and then extracting the thin-walled part region  569 . The thin-walled part data  59  includes both of the thin-walled part region  569  of the first thin-walled part data  591  and the thin-walled part region  569  of the second thin-walled part data  592 . 
     According to the present modification, learning of the classification model  31  can be performed by weighting the thin-walled part region  569  for each of a plurality of types of regions. Therefore, a learning model generation method that generates the classification model  31  configured to accurately classify a thinly drawn region for each of a plurality of types of regions can be provided. 
     [Modification 1-7] 
       FIG.  16    is an explanatory diagram explaining a thin-walled part extraction model  32 . The thin-walled part extraction model  32  is a model that receives an input of the tomographic image  58  and outputs the thin-walled part data  59 . 
       FIG.  17    is an explanatory diagram explaining a record layout of a thin-walled part training DB. The thin-walled part training DB is used for machine learning of the thin-walled part extraction model  32 . The thin-walled part training DB includes a tomographic image field and a correct answer thin-walled part data field. Each of the tomographic image field and the correct answer thin-walled part data field has two subfields, namely, an RT format field and an XY format field. 
     The RT format field of the tomographic image field records the tomographic image  58  in the RT format formed by arranging scanning line data in parallel in the order of the scanning angle. The XY format field of the tomographic image field records the tomographic image  58  in the XY format generated by conducting coordinate transformation on the tomographic image  58  in the RT format. 
     The RT format field of the correct answer thin-walled part data field records the thin-walled part data  59  in the RT format. The XY format field of the correct answer thin-walled part data field records the thin-walled part data  59  in the XY format. The thin-walled part data  59  of the thin-walled part training DB is generated using, for example, the program described with reference to  FIG.  9   . Note that, in the lower records illustrated in  FIG.  17   , the thin-walled part region  569  is not detected. 
     Note that the tomographic image  58  in the XY format may be generated by coordinate transformation from the tomographic image  58  in the RT format if applicable, instead of being recorded in the thin-walled part training DB. Only one of the correct answer thin-walled part data in the RT format and the correct answer thin-walled part data in the XY format may be recorded in the thin-walled part training DB, and the other may be generated by coordinate transformation if applicable. The thin-walled part training DB has one record for one set of thin-walled part training data. 
     Returning to  FIG.  16   , the description will be continued. Machine learning of the thin-walled part extraction model  32  that outputs the thin-walled part data  59  when the tomographic image  58  is input is performed using the thin-walled part training DB. Here, the thin-walled part extraction model  32  is, for example, a model having a U-Net structure that implements semantic segmentation. The thin-walled part extraction model  32  may be a Mask R-CNN model or any other model that implements segmentation of an image. 
     An outline of a machine learning method will be described. One set of thin-walled part training data is acquired from the thin-walled part training DB. The tomographic image  58  is input to the thin-walled part extraction model  32  in the middle of learning, and the thin-walled part data  59  is output. The parameters of the thin-walled part extraction model  32  are adjusted such that the thin-walled part data  59  output from the thin-walled part extraction model  32  matches the thin-walled part data  59  recorded in the thin-walled part training data. 
     After the appropriate thin-walled part extraction model  32  is generated, the thin-walled part data  59  can be generated with a small computation amount by using the thin-walled part extraction model  32 . 
     [Modification 1-8] 
     The present modification relates to a generation method for the classification model  31  that uses the thin-walled part data  59  as hint information. Description of parts common to the first embodiment will not be repeated. 
       FIG.  18    is an explanatory diagram explaining a generation method for the classification model  31  according to a modification 1-8. Machine learning of the classification model  31  is performed using the classification training DB  41 . Here, the classification model  31  is a model that receives inputs of the tomographic image  58  and the thin-walled part data  59  and uses the thin-walled part data  59  as the hint information having a high correct answer probability, to output the output classification data  51 . 
     An outline of a machine learning method will be described. One set of classification training data is acquired from the classification training DB  41 . The tomographic image  58  is input to the thin-walled part extraction model  32  described with reference to  FIG.  15   , and the thin-walled part data  59  is output. Note that the thin-walled part data  59  may be created by the program described with reference to  FIG.  10   . 
     The tomographic image  58  and the thin-walled part data  59  are input to the classification model  31 , and the output classification data  51  is output. The difference data  55  is generated based on the comparison between the output classification data  51  and the correct answer classification data  57 . The loss value  551  is defined based on the difference data  55 . Parameter adjustment for the classification model  31  is performed using, for example, the back propagation method such that the loss value  551  approaches a predetermined value. 
     Second Embodiment 
     The present embodiment relates to a machine learning method and the like that adjust the parameters of a classification model  31 , using weighted correct answer classification data  66  obtained by weighting correct answer classification data  57  based on thin-walled part data  59 . Description of parts common to the first embodiment will not be repeated. 
       FIGS.  19  to  22    are explanatory diagrams explaining a generation method for the classification model  31  according to a second embodiment. Classification training data in which a tomographic image  58  and the correct answer classification data  57  are combined as a set is recorded in a classification training DB  41 . Based on the correct answer classification data  57 , a control unit  21  generates the thin-walled part data  59  obtained by extracting a thin-walled part region  569  thinner than a predetermined threshold value for a specified region. 
     The control unit  21  generates the weighted correct answer classification data  66  obtained by weighting the portion of the thin-walled part region  569  in the correct answer classification data  57 . A specific example of the weighted correct answer classification data  66  will be described with reference to  FIG.  20   . 
     The upper left part of  FIG.  20    schematically illustrates nine pixels of the correct answer classification data  57 . Note that the correct answer classification data  57  in  FIG.  20    is the same data as the correct answer classification data  57  in  FIG.  7    and is displayed using numerical values of “0” and “1” instead of the percentage display. The right part of  FIG.  20    schematically illustrates the thin-walled part data  59 . 
     The lower left part of  FIG.  20    schematically illustrates the weighted correct answer classification data  66 . In the following description, each piece of data constituting the weighted correct answer classification data  66  is also described as a “pixel” similarly to the data included in the tomographic image  58 . Each group of the nine pixels illustrated in  FIG.  20    indicates a group of pixels located at the corresponding place. The pixels in the center and the lower center on which the rounded rectangles are displayed are pixels corresponding to the thin-walled part region  569 . 
     The control unit  21  calculates the weighted correct answer classification data  66  by formula (7). 
     
       
         
           
             
               
                 
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     Dij indicates correct answer data for the j-th label of the i-th pixel. 
     Dwij indicates weighted correct answer data for the j-th label of the i-th pixel. 
     m indicates a constant greater than one. 
     In the example illustrated in  FIG.  20   , the constant m is three. The data of the pixel corresponding to the thin-walled part region  569  has a value of three times the data of the other pixels. In the weighted correct answer classification data  66 , the sum of the respective labels is m for the pixels in the thin-walled part region  569 , and the sum of the respective labels is one for the pixels in a region other than the thin-walled part region  569 . 
     Returning to  FIG.  19   , the description will be continued. The control unit  21  inputs the tomographic image  58  to the classification model  31  in the middle of learning and acquires output classification data  51 . The control unit  21  generates difference data  55  based on the comparison between the output classification data  51  and the weighted correct answer classification data  66 . 
     The upper left part of  FIG.  21    schematically illustrates nine pixels of the weighted correct answer classification data  66 . The upper right part of  FIG.  21    schematically illustrates nine pixels of the output classification data  51 . The lower part of  FIG.  21    schematically illustrates nine pixels of the difference data  55 . Each group of the nine pixels illustrated in  FIG.  21    indicates a group of pixels located at the corresponding place. For the correct answer classification data  57  and the difference data  55 , the pixels in the center and the lower center on which the rounded rectangles are displayed are pixels corresponding to the thin-walled part region  569 . 
     The control unit  21  generates the difference data  55  in which losses relating to each label of each pixel are recorded, by formula (8). 
       [Math. 8] 
         E   ij   =|Dw   ij   −Q   ij |  (8)
 
     Eij indicates the loss relating to the j-th label of the i-th pixel. 
     Qij indicates a probability that the i-th pixel has the j-th label in the output classification data. 
     Returning to  FIG.  19   , the description will be continued. The control unit  21  calculates a representative value of respective pixels constituting the difference data  55  to generate weighted difference data  65 .  FIG.  22    illustrates the difference data  55  illustrated in the lower part of  FIG.  21    and the weighted difference data  65  generated based on this difference data  55 . In the example illustrated in  FIG.  22   , for each pixel of the difference data  55 , the control unit  21  generates the weighted difference data  65 , using the loss with respect to the correct answer label as the representative value. In this case, the control unit  21  does not have to calculate the loss corresponding to the incorrect answer label. The losses of the pixels corresponding to the thin-walled part region  569  surrounded by the rounded rectangles have obviously greater values than the losses of the pixels in a region other than the thin-walled part region  569 . 
     The control unit  21  calculates the loss value  551 , based on the weighted difference data  65 . The control unit  21  performs parameter adjustment for the classification model  31  using, for example, the back propagation method such that the loss value  551  approaches a predetermined value. By machine learning in which parameter adjustment is repeated using a large number of pieces of the classification training data, the classification model  31  configured to accurately classify even a portion corresponding to the thin-walled part region  569  is generated. 
     [Modification 2-1] 
       FIG.  23    is an explanatory diagram explaining the weighted correct answer classification data  66  according to a modification 2-1. In the present modification, for each pixel of the difference data  55 , the control unit  21  generates the weighted difference data  65 , using the total sum of losses with respect to all the labels as the representative value. Also in the present modification, the losses of the pixels corresponding to the thin-walled part region  569  surrounded by the rounded rectangles have obviously greater values than the losses of the pixels in a region other than the thin-walled part region  569 . 
     According to the present embodiment, the loss value  551  can be calculated by simple addition and integration without using natural logarithms. 
     Third Embodiment 
     The present embodiment relates to a generation method for a classification model  31  that defines a loss value  551  based on the distance between boundary lines  53  between a first lumen region  561  and a living tissue region  566 . Description of parts common to the first embodiment will not be repeated. 
       FIGS.  24  and  25    are explanatory diagrams explaining the loss value  551  according to a third embodiment. In  FIG.  24   , each diagram is illustrated in the RT format. The upper left part of  FIG.  24    illustrates correct answer classification data  57  recorded in classification training data. 
     The lower left part of  FIG.  24    illustrates a diagram in which the boundary lines  53  indicating edges of the living tissue region  566  are superimposed on the thin-walled part data  59  generated based on the correct answer classification data  57 . 
     The living tissue region  566  at a portion sandwiched between the first lumen region  561  located at the left end of the correct answer classification data  57  and a vertically long second lumen region  562  forms a thin-walled part region  569 . 
     The upper right part of  FIG.  24    illustrates output classification data  51  output from the classification model  31  when a tomographic image  58  recorded in the classification training data is input to the classification model  31  being trained. When the correct answer classification data  57  is compared with the output classification data  51 , the vicinity of the thin-walled part region  569  is not accurately classified. 
       FIG.  25    is a diagram in which the XXV portion of the output classification data  51  is enlarged and the thin-walled part region  569  indicated by thin horizontal line hatching is superimposed. An output boundary line  531  that is a boundary between the living tissue region  566  and the first lumen region  561  in the output classification data  51  is indicated by a thick line. A correct answer boundary line  537  that is a boundary between the living tissue region  566  and the first lumen region  561  in the correct answer classification data  57  is indicated by a solid line. In the state illustrated in  FIG.  24   , learning of the classification model  31  has progressed to such an extent that the correct answer boundary line  537  substantially matches the output boundary line  531  except in the vicinity of the thin-walled part region  569 . 
     The control unit  21  generates a determination line  538  connecting each pixel on the output boundary line  531  and the correct answer boundary line  537  at the shortest distance. In the following description, the end part of the determination line  538  on the side of the output boundary line  531  will be described as a start point, and the end part of the determination line  538  on the side of the correct answer boundary line  537  will be described as an end point. 
     The control unit  21  calculates the length of the determination line  538 . The length of the determination line  538  indicates the distance between the correct answer boundary line  537  and the output boundary line  531  and corresponds to the loss of each pixel on the output boundary line  531 . The control unit  21  calculates the loss value  551  such that a pixel whose end point of the determination line  538  is in contact with the thin-walled part region  569  has a stronger influence than a pixel whose end point of the determination line  538  is not in contact with the thin-walled part region  569 . A specific example will be given and described. 
     The control unit  21  calculates the loss value  551  based on, for example, formula (9). 
     
       
         
           
             
               
                 
                   [ 
                   
                     Math 
                     . 
                         
                     9 
                   
                   ] 
                 
               
               
                  
               
             
             
               
                 
                   
                     Loss 
                     ⁢ 
                         
                     value 
                   
                   = 
                   
                     
                       1 
                       P 
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         P 
                       
                       
                         ( 
                         
                           Li 
                           · 
                           Gi 
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   9 
                   ) 
                 
               
             
           
         
       
     
     Li indicates the length of the determination line  538  whose start point is the i-th pixel. 
     Gi indicates a weight relating to the thin-walled part region. 
     When the end point of the determination line whose start point is the i-th pixel is not in the thin-walled part region, Gi=1 holds. 
     When the end point of the determination line whose start point is the i-th pixel is in the thin-walled part region, Gi=m holds. 
     P indicates the number of pixels on the output boundary line. 
     m indicates a constant greater than one. 
     Formula (9) indicates that the loss value  551  is defined such that the contact of the end point of the determination line  538  with the thin-walled part has a weight of m times with respect to the non-contact with the thin-walled part. The constant m is, for example, 100. 
       FIG.  26    is a flowchart explaining a processing flow of a program according to the third embodiment. Since the processes from step S 501  to step S 503  are the same as the processes of the program according to the first embodiment described with reference to  FIG.  9   , the description thereof will not be repeated. 
     The control unit  21  activates a subroutine for loss value calculation (step S 551 ). The subroutine for loss value calculation is a subroutine that calculates the loss value  551  based on formula (9). The processing flow of the subroutine for loss value calculation will be described later. 
     The control unit  21  performs parameter adjustment for the classification model  31  using, for example, the back propagation method such that the loss value  551  approaches a predetermined value (step S 507 ). Since the subsequent processing flow is the same as the processing flow of the program according to the first embodiment described with reference to  FIG.  9   , the description thereof will not be repeated. 
       FIG.  27    is a flowchart explaining a processing flow of the subroutine for loss value calculation. The subroutine for loss value calculation is a subroutine that calculates the loss value  551  based on formula (9). 
     The control unit  21  extracts the correct answer boundary line  537  from the correct answer classification data  57  (step S 561 ). The control unit  21  extracts the output boundary line  531  from the output classification data  51  (step S 562 ). The control unit  21  generates a composite image in which the correct answer boundary line  537  and the output boundary line  531  are placed in one image (step S 563 ). The control unit  21  executes the subsequent processes using the composite image. 
     The control unit  21  selects the start point from among the pixels on the output boundary line  531  (step S 564 ). The control unit  21  generates the determination line  538  connecting the start point selected in step S 563  and the correct answer boundary line  537  at the shortest distance (step S 565 ). The control unit  21  determines whether the end point of the determination line  538  is in contact with the thin-walled part region  569  (step S 566 ). 
     When determining that the determination line  538  is in contact with the thin-walled part region  569  (YES in step S 566 ), the control unit  21  records a value obtained by weighting the length of the determination line  538  (step S 567 ). When determining that the determination line  538  is not in contact with the thin-walled part region  569  (NO in step S 566 ), the control unit  21  records the length of the determination line  538  (step S 568 ). 
     After step S 567  or S 568  ends, the control unit  21  determines whether the process for all the pixels on the output boundary line  531  has ended (step S 569 ). When determining that the process has not ended (NO in step S 569 ), the control unit  21  returns to step S 564 . 
     When determining that the process has ended (YES in step S 569 ), the control unit  21  calculates a mean value of the values recorded in steps S 567  and S 568  (step S 570 ). The mean value calculated in step S 570  is the loss value  551 . Thereafter, the control unit  21  ends the process. 
     Note that the output boundary line  531  with which the control unit  21  calculates the loss value  551  is not limited to the boundary line  53  between the first lumen region  561  and the living tissue region  566 . Machine learning of the classification model  31  can be performed based on the loss value  551  for the boundary line  53  between any regions. 
     In step S 570 , the control unit  21  may calculate a representative value such as a median value or a mode value instead of the mean value. In step S 570 , the control unit  21  may calculate a geometric mean value or a harmonic mean value instead of the arithmetic mean value indicated by formula (4). In the subroutine for loss value calculation, the control unit  21  may set the start points at predetermined intervals instead of sequentially setting the start points at all the pixels on the output boundary line  531 . 
     According to the present embodiment, machine learning of the classification model  31  can be performed such that the entire shape of the output boundary line  531  approaches the correct answer boundary line  537 . For example, after machine learning of the classification model  31  is performed by a normal approach that does not use the thin-walled part data  59  or the approach of the first embodiment, additional learning may be performed by the approach of the present embodiment. 
     Fourth Embodiment 
     The present embodiment relates to a generation method for a classification model  31  that calculates a loss value  551  based on whether correct answer classification data  57  matches output classification data  51 . Description of parts common to the first embodiment will not be repeated. 
       FIG.  28    is an explanatory diagram explaining difference data  55  according to a fourth embodiment. The upper left part of  FIG.  28    schematically illustrates nine pixels of the correct answer classification data  57 . The upper right part of  FIG.  28    schematically illustrates nine pixels of the output classification data  51 . The lower part of  FIG.  28    schematically illustrates nine pixels of the difference data  55 . The positions of the groups of the nine pixels illustrated in  FIG.  28    correspond to each other. 
     Note that, in  FIG.  28   , one label representing the correct answer is recorded in each pixel of the correct answer classification data  57 , and one label with the highest probability is recorded in each pixel of the output classification data  51 . When the labels of the corresponding pixels of the correct answer classification data  57  and the output classification data  51  match with each other, the control unit  21  records a label indicating the “correct answer” in the corresponding pixel of the difference data  55  and, when the labels do not match with each other, records a label indicating the “incorrect answer”. 
     The control unit  21  calculates the loss value  551  such that the discrepancy between the “correct answer” and the “incorrect answer” in the pixel included in the thin-walled part region  569  has a stronger influence than the discrepancy between the “correct answer” and the “incorrect answer” in the pixel in a region other than the thin-walled part region  569 . A specific example will be given and described. 
     The control unit  21  calculates the loss value  551  based on, for example, formula (10). 
     
       
         
           
             
               
                 
                   [ 
                   
                     Math 
                     . 
                         
                     10 
                   
                   ] 
                 
               
               
                  
               
             
             
               
                 
                   
                     Loss 
                     ⁢ 
                         
                     value 
                   
                   = 
                   
                     
                       1 
                       C 
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         C 
                       
                       
                         ( 
                         
                           Fi 
                           · 
                           Gi 
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   10 
                   ) 
                 
               
             
           
         
       
     
     Fi indicates the loss of the i-th pixel. 
     When the i-th pixel has the “incorrect answer”, Fi=k holds. 
     When the i-th pixel has the “correct answer”, Fi=0 holds. 
     Gi indicates a weight relating to the thin-walled part region. 
     When the i-th pixel falls under the thin-walled part region, Gi=m holds. 
     When the i-th pixel does not fall under the thin-walled part region, Gi=1 holds. 
     C indicates the number of pixels. 
     k indicates a constant that is a positive value. 
     m indicates a constant greater than one. 
     Formula (10) indicates that the loss value  551  is defined such that the incorrect answer given to the pixel in the thin-walled part region  569  has a weight of m times with respect to the incorrect answer given to the pixel in a region other than the thin-walled part region  569 . The constant m is, for example, 100. 
     This will be described more specifically. When the number of pixels located in the thin-walled part region  569  is A and the number of pixels located in a region other than the thin-walled part region  569  is B among the pixels having the “incorrect answer”, the loss value  551  has a value indicated by formula (11). 
     
       
         
           
             
               
                 
                   [ 
                   
                     Math 
                     . 
                         
                     11 
                   
                   ] 
                 
               
               
                  
               
             
             
               
                 
                   
                     Loss 
                     ⁢ 
                         
                     value 
                   
                   = 
                   
                     
                       k 
                       C 
                     
                     ⁢ 
                     
                       ( 
                       
                         
                           m 
                           ⁢ 
                           A 
                         
                         + 
                         B 
                       
                       ) 
                     
                   
                 
               
               
                 
                   ( 
                   11 
                   ) 
                 
               
             
           
         
       
     
     The control unit  21  defines a combination of parameters of the classification model  31  such that the loss value  551  approaches a predetermined value, using an approach such as the grid search, random search, or Bayesian optimization. By repeating parameter adjustment for the classification model  31  using a large number of pieces of classification training data, the control unit  21  performs machine learning of the classification model  31  such that the classification model  31  outputs the appropriate output classification data  51  when a tomographic image  58  is input. 
     According to the present embodiment, the classification model  31  can be generated using an algorithm different from the back propagation method. 
     Fifth Embodiment 
     The present embodiment relates to a machine learning method and the like in which a threshold value for determining a thin-walled part region  569  is set to be greater at an initial stage of learning of a classification model  31  and the threshold value is reduced as learning progresses. Description of parts common to the first embodiment will not be repeated. 
       FIG.  29    is a flowchart explaining a processing flow of a program according to a fifth embodiment. A control unit  21  sets the threshold value for determining the thin-walled part region  569 , which is used when generating thin-walled part data  59  based on correct answer classification data  57 , to a predetermined value (step S 601 ). 
     The control unit  21  acquires one set of classification training data from a classification training DB  41  (step S 501 ). Since the subsequent processes up to step S 507  are the same as the processes of the program according to the first embodiment described with reference to  FIG.  9   , the description thereof will not be repeated. 
     The control unit  21  determines whether to shift to the next stage (step S 611 ). For example, when a predetermined number of pieces of the classification training data have been learned, the control unit  21  determines to shift to the next stage. For example, when the loss value  551  or the amount of adjustment of the parameters falls below a predetermined threshold value, the control unit  21  may determine to shift to the next stage. 
     When determining not to shift to the next stage (NO in step S 611 ), the control unit  21  returns to step S 501 . When determining to shift to the next stage (YES in step S 611 ), the control unit  21  determines whether to change the threshold value for determining the thin-walled part region  569  (step S 612 ). For example, when the threshold value has reached a predetermined minimum value, the control unit  21  determines not to change the threshold value. 
     When determining to change the threshold value (YES in step S 612 ), the control unit  21  returns to step S 601  and sets the threshold value to a value smaller than the value in the previous loop. When determining not to change the threshold value (NO in step S 612 ), the control unit  21  records the adjusted parameters of the classification model  31  in an auxiliary storage device  23  (step S 613 ). Thereafter, the control unit  21  ends the process. As described above, the generation of the classification model  31  ends. 
     A specific example will be given and described. In an initial stage of machine learning, a threshold value for determining first thin-walled part data  591  is set to about five millimeters. As the machine learning progresses, the threshold value is gradually reduced and finally, is set to a target value of about one millimeter. By setting in this manner, the parameters of the classification model  31  can be efficiently adjusted. 
     According to the present embodiment, machine learning of the classification model  31  can be efficiently carried out. 
     Sixth Embodiment 
     The present embodiment relates to a catheter system  10  that generates a three-dimensional image in real time, using a three-dimensional scanning image acquisition catheter  28 . Description of parts common to the first embodiment will not be repeated. 
       FIG.  30    is an explanatory diagram explaining a configuration of a catheter system  10  according to a sixth embodiment. The catheter system  10  includes an image processing apparatus  230 , a catheter control device  27 , a motor driving unit (MDU)  289 , and an image acquisition catheter  28 . The image acquisition catheter  28  is connected to the image processing apparatus  230  via the MDU  289  and the catheter control device  27 . 
     The image acquisition catheter  28  includes a sheath  281 , a shaft  283  introduced through the inside of the sheath  281 , and a sensor  282  disposed at a distal end of the shaft  283 . The MDU  289  rotates and advances and retracts the shaft  283  and the sensor  282  inside the sheath  281 . 
     The catheter control device  27  generates one tomographic image  58  for each rotation of the sensor  282 . By the operation on the MDU  289  to rotate the sensor  282  while pulling or pushing the sensor  282 , the catheter control device  27  continuously generates a plurality of tomographic images  58  substantially perpendicular to the sheath  281 . 
     The image processing apparatus  230  includes a control unit  231 , a main storage device  232 , an auxiliary storage device  233 , a communication unit  234 , a display unit  235 , an input unit  236 , and a bus. The control unit  231  is an arithmetic control device that executes a program of the present embodiment. For the control unit  231 , one or a plurality of CPUs or GPUs, a multi-core CPU, or the like is used. The control unit  231  is connected to each hardware unit constituting the image processing apparatus  230  via the bus. 
     The main storage device  232  is a storage device such as an SRAM, a DRAM, or a flash memory. In the main storage device  232 , information involved in the middle of the process performed by the control unit  231  and the program being executed by the control unit  231  are temporarily saved. 
     The auxiliary storage device  233  is a storage device such as an SRAM, a flash memory, a hard disk, or a magnetic tape. In the auxiliary storage device  233 , the classification model  31  described with reference to the first to fourth embodiments, a program to be executed by the control unit  231 , and various sorts of data involved in executing the program are saved. The classification model  31  is an example of a trained model of the present embodiment. 
     The communication unit  234  is an interface that performs communication between the image processing apparatus  230  and a network. The classification model  31  may be stored in an external mass storage device or the like connected to the image processing apparatus  230 . 
     For example, the display unit  235  is a liquid crystal display panel, an organic EL panel, or the like. For example, the input unit  236  is a keyboard, a mouse, or the like. The input unit  236  may be stacked on the display unit  235  to constitute a touch panel. The display unit  235  may be a display device connected to the image processing apparatus  230 . 
     The image processing apparatus  230  is dedicated hardware used in combination with the catheter control device  27 , for example. The image processing apparatus  230  and the catheter control device  27  may be integrally configured. The image processing apparatus  230  may be a general-purpose personal computer, a tablet, a large computing machine, or a virtual machine that works on a large computing machine. The image processing apparatus  230  may be constituted by a plurality of personal computers that perform distributed processing, or hardware such as a large computing machine. The image processing apparatus  230  may be constituted by a cloud computing system or a quantum computer. 
     The control unit  231  successively acquires the tomographic images  58  from the catheter control device  27 . The control unit  231  inputs each tomographic image  58  to the classification model  31  and acquires output classification data  51  that has been output. The control unit  21  generates a three-dimensional image based on a plurality of pieces of the output classification data  51  acquired in time series and outputs the generated three-dimensional image to the display unit  235 . As described above, so-called three-dimensional scanning is performed. 
     The advancing and retracting operation of the sensor  282  includes both of an operation of advancing and retracting the entire image acquisition catheter  28  and an operation of advancing and retracting the sensor  282  inside the sheath  281 . The advancing and retracting operation may be automatically performed at a predetermined speed by the MDU  289  or may be manually performed by the user. 
     Note that the image acquisition catheter  28  is not limited to a mechanical scanning mechanism that mechanically performs rotation and advancement and retraction. For example, the image acquisition catheter  28  may be an electronic radial scanning image acquisition catheter  28  using the sensor  282  in which a plurality of ultrasound transducers is annularly disposed. Instead of the image acquisition catheter  28 , a transesophageal echocardiography (TEE) probe or an extracorporeal ultrasound probe may be used. 
       FIG.  31    is a flowchart explaining a processing flow of a program according to the sixth embodiment. When receiving an instruction to start three-dimensional scanning from the user, the control unit  231  executes a program to be described with reference to  FIG.  31   . 
     The control unit  231  instructs the catheter control device  27  to start three-dimensional scanning (step S 581 ). The catheter control device  27  controls the MDU  289  to start three-dimensional scanning. The control unit  231  acquires one tomographic image  58  from the catheter control device  27  (step S 582 ). In step S 582 , the control unit  231  implements the function of an image acquisition unit of the present embodiments. 
     The control unit  231  inputs the tomographic image  58  to the classification model  31  and acquires the output classification data  51  that has been output (step S 583 ). In step S 583 , the control unit  231  implements the function of a classification data acquisition unit of the present embodiments. The control unit  231  records the output classification data  51  in the auxiliary storage device  233  or the communication unit  234  (step S 584 ). 
     The control unit  231  displays the three-dimensional image generated based on the output classification data  51  recorded in time series, on the display unit  235  (step S 585 ). The control unit  231  determines whether to end the process (step S 586 ). For example, when a series of three-dimensional scanning has ended, the control unit  231  determines to end the process. 
     When determining not to end the process (NO in step S 586 ), the control unit  231  returns to step S 582 . When determining to end the process (YES in step S 586 ), the control unit  231  ends the process. 
     According to the present embodiment, the catheter system  10  equipped with the classification model  31  described in the first to fourth embodiments can be provided. According to the present embodiment, the catheter system  10  that displays an appropriate segmentation result even when a thin portion exists in the site to be scanned can be provided. 
     According to the present embodiment, since segmentation can be performed appropriately, the catheter system  10  that displays a three-dimensional image with less noise can be provided. Furthermore, the catheter system  10  configured to appropriately perform automatic measurement of the area, the volume, and the like can be provided. 
     Seventh Embodiment 
     The present embodiment relates to a mode that implements an information processing apparatus  20  of the present embodiment by causing a general-purpose computer  90  and a program  97  to work in combination. Description of parts common to the first embodiment will not be repeated. 
       FIG.  32    is an explanatory diagram explaining a configuration of the information processing apparatus  20  according to a seventh embodiment. The computer  90  includes a control unit  21 , a main storage device  22 , an auxiliary storage device  23 , a communication unit  24 , a display unit  25 , an input unit  26 , a reading unit  29 , and a bus. The computer  90  is a general-purpose personal computer, a tablet, a smartphone, a large computing machine, a virtual machine working on a large computing machine, a cloud computing system, or a quantum computer. The computer  90  may be made up of a plurality of personal computers or the like that performs distributed processing. 
     The program  97  is recorded in a portable recording medium  96 . The control unit  21  reads the program  97  via the reading unit  29  and saves the read program  97  in the auxiliary storage device  23 . In addition, the control unit  21  may read the program  97  stored in a semiconductor memory  98  such as a flash memory mounted in the computer  90 . Furthermore, the control unit  21  may download the program  97  from another server computer (not illustrated) connected via the communication unit  24  and a network (not illustrated) and save the downloaded program  97  in the auxiliary storage device  23 . 
     The program  97  is installed as a control program for the computer  90  and is loaded into the main storage device  22  to be executed. This causes the computer  90  to function as the information processing apparatus  20  described above. The program  97  is an example of a program product. 
     Eighth Embodiment 
       FIG.  33    is a functional block diagram of an information processing apparatus  20  according to an eighth embodiment. The information processing apparatus  20  includes a training data acquisition unit  71 , a thin-walled part data acquisition unit  72 , and a parameter adjustment unit  73 . 
     The training data acquisition unit  71  acquires training data from a training database  41  that records a plurality of sets of a tomographic image  58  acquired using a tomographic image acquisition probe  28 , and correct answer classification data  57  in which each pixel included in the tomographic image  58  is classified into a plurality of regions including a living tissue region  566  and a non-living tissue region, in association with each other. 
     The thin-walled part data acquisition unit  72  acquires thin-walled part data  59  relating to a range of a thin-walled part thinner than a predetermined threshold value for a predetermined region in the correct answer classification data  57 . The parameter adjustment unit  73  performs a parameter adjustment process for a learning model  31  that outputs output classification data  51  obtained by classifying each pixel included in the tomographic image  58  into the plurality of regions, based on the training data and the thin-walled part data  59 . 
     Ninth Embodiment 
       FIG.  34    is a functional block diagram of an image processing apparatus  230  according to a ninth embodiment. The image processing apparatus  230  includes an image acquisition unit  76  and a classification data acquisition unit  77 . 
     The classification data acquisition unit  77  acquires a tomographic image  58  obtained using a tomographic image acquisition probe  28 . The classification data acquisition unit  77  inputs the tomographic image  58  to a trained model  31  generated by the above-described method and acquires output classification data  51 . 
     The technical features (components) described in each embodiment can be combined with each other, and new technical features can be formed by the combination. 
     It is supposed that the embodiments disclosed herein are considered to be an example in all respects and not to be restrictive. The scope of the present invention is indicated not by the above meaning but by the claims and is intended to include all changes within the meaning and scope equivalent to the claims.