Patent Publication Number: US-2023162357-A1

Title: Medical image processing device and method of operating the same

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority under 35 U.S.C § 119(a) to Japanese Patent Application No. 2021-188619 filed on 19 Nov. 2021. The above application is hereby expressly incorporated by reference, in its entirety, into the present application. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to a medical image processing device and a method of operating the medical image processing device. 
     2. Description of the Related Art 
     In recent years, detecting an object, such as people and an animal, from an input image using machine learning, such as deep learning, has been disseminated in the field of image processing. In such machine learning for detecting an object, machine learning is often performed using a rectangular region, which is called a bounding box including an object to be detected, as correct answer data. Further, a plurality of correct answer data are accumulated and machine learning is performed, so that a discriminator is generated. The discriminator extracts an object to be detected from an input image and performs convolutional calculation to discriminate the type of the object and to extract the position of the object. 
     The above-mentioned machine learning for detecting an object also has been disseminated in a medical field, and a computer aided diagnosis (hereinafter, referred to as CAD) system, which analyzes a medical image, such as an endoscopic image, to automatically detect an object to be detected, such as a lesion, and performs the highlighting or the like of the detected object to be detected, is known. 
     Meanwhile, in machine learning for detecting a lesion, a medical doctor inputs a coordinate position in a medical image using an input device and uses a bounding box as a rectangular region, which includes a lesion, as correct answer data (see WO2019/230302A, corresponding to US2021/052137A1). Then, the medical doctor associates the bounding box with the medical image and records the bounding box and the medical image or inputs the bounding box and the medical image to a learning unit for machine learning. 
     SUMMARY OF THE INVENTION 
     However, in the detection of a lesion in a medical field, it may not be suitable to specify a lesion using a rectangular region depending on the shape of the lesion. In a case where the image of the inside of an organ having a tubular shape, for example, the lumen of the esophagus is picked up with an endoscope, a circumferential lesion circling the lumen in a circumferential direction may be present. The circumferential lesion has the shape of, for example, a doughnut or a crescent in an endoscopic image. In a case where a bounding box, which is a rectangular region, is acquired for the lesion having such a shape, most of a region inside the bounding box may be unrelated to the lesion. For this reason, the bounding box cannot be used as correct answer data for machine learning. 
     Further, in a case where a bounding box is acquired for a lesion having the shape of a doughnut or a crescent and machine learning is performed using the bounding box as correct answer data, a normal mucous membrane portion, which occupies most of a region in the bounding box, and a boundary between the shadow of the back of the lumen and a mucous membrane are learned. For this reason, there is a possibility that a lesion may be detected in an input image having the same angle even through there is actually no lesion. 
     An object of the present invention is to provide a medical image processing device that can acquire a bounding box suitable for machine learning from a medical image and a method of operating the medical image processing device. 
     A medical image processing device according to an aspect of the present invention comprises a processor, and the processor acquires a medical image, acquires a bounding box that corresponds to an object to be detected shown in the acquired medical image and indicates a first region in which at least a part of the object to be detected is included, makes an adjustment for changing a position of the bounding box in the medical image and reducing an area of the bounding box on the basis of the medical image, and associates a new adjusted bounding box with the medical image. 
     It is preferable that the processor acquires the object to be detected shown in the medical image as a second region, analyzes brightness information or pixel information of the first region and the second region, and adjusts the bounding box in a case where a result of the analysis satisfies a certain condition. It is preferable that the processor extracts a contour of the object to be detected from the medical image and acquires a region inside the contour as the second region. It is preferable that the processor analyzes the medical image, extracts the contour of the object to be detected according to a brightness value or a pixel value of the medical image, and acquires the second region. 
     It is preferable that the processor calculates a ratio of the second region to the first region as the analysis and adjusts the bounding box in a case where the ratio is equal to or less than a certain threshold value. 
     It is preferable that the processor extracts a low-brightness region having a brightness value equal to or less than a certain value in the first region and adjusts the bounding box in a case where a ratio of the low-brightness region to the first region is equal to or larger than a certain threshold value. 
     It is preferable that the processor calculates a ratio of the bounding box to the medical image and adjusts the bounding box in a case where the ratio is equal to or larger than a certain threshold value. 
     It is preferable that the processor adjusts the bounding box in a case where a distance between a center or a centroid of the bounding box and a center of the medical image is equal to or less than a certain threshold value. 
     It is preferable that the processor uses brightness values or pixel values of the first region and the second region to calculate an object-to-be-detected centroid that is a centroid of the object to be detected, an object-to-be-detected moment that is a moment around the object-to-be-detected centroid, and a bounding box moment that is a moment of the bounding box around the object-to-be-detected centroid, and changes a centroid of the bounding box and a width and a height of the bounding box using the object-to-be-detected centroid, the object-to-be-detected moment, and the bounding box moment, as the adjustment. 
     It is preferable that, as the adjustment, the processor causes a position of the centroid of the bounding box to coincide with a position of the object-to-be-detected centroid, calculates a ratio of the bounding box moment to the object-to-be-detected moment, and reduces the width and/or the height and calculates the bounding box moment again in a case where the ratio exceeds a certain threshold value and determines the width and the height in a case where the ratio is equal to or less than the certain threshold value. 
     It is preferable that the processor excludes a low-brightness region having a brightness value equal to or less than a certain value in the bounding box and calculates a new bounding box, which is circumscribed about a new second region of the medical image excluding the low-brightness region, as the adjustment. 
     It is preferable that, in a case where the second region is divided into regions since the low-brightness region is excluded, the processor calculates new bounding boxes circumscribed about the divided regions of the second region, respectively. 
     It is preferable that the medical image and the new adjusted bounding box associated with the medical image are stored. 
     It is preferable that the medical image processing device further comprises a learning unit to which the medical image and the new adjusted bounding box associated with the medical image are input and which performs machine learning for the object to be detected. 
     It is preferable that the medical image is an endoscopic image obtained in a case where an image of a lumen in a body having a dimension in a depth direction larger than a dimension in a radial direction is picked up with an endoscope, and the object to be detected is a lesion area. 
     It is preferable that, in a case where the processor is to acquire the bounding box not yet adjusted, the processor receives the bounding box input by a user. 
     It is preferable that, in a case where the processor is to acquire the bounding box not yet adjusted, the processor extracts the object to be detected shown in the medical image and calculates the bounding box circumscribed about the extracted object to be detected. 
     A method of operating a medical image processing device according to another aspect of the present invention comprises a step of acquiring a medical image, a step of acquiring a bounding box that corresponds to an object to be detected shown in the acquired medical image and indicates a first region in which at least a part of the object to be detected is included, a step of making an adjustment for changing a position of the bounding box in the medical image and reducing an area of the bounding box on the basis of the medical image, and a step of associating a new adjusted bounding box with the medical image. 
     According to the present invention, it is possible to acquire a bounding box suitable for machine learning from a medical image. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram showing the functions of a medical image processing system and an endoscope system. 
         FIG.  2    is a block diagram showing the functions of a medical image processing device. 
       (A) and (B) of  FIG.  3    are a diagram illustrating the acquisition of a bounding box. 
       (A) and (B) of  FIG.  4    are a diagram illustrating an example of the adjustment of the bounding box using the brightness information of a first region and a second region. 
         FIG.  5    is a flowchart related to the adjustment and storage of a bounding box of a first embodiment. 
         FIG.  6    is a block diagram showing the functions of a medical image processing device according to a second embodiment. 
         FIG.  7    is a flowchart related to the adjustment of a bounding box of a second embodiment and an input of the bounding box to a learning unit. 
       (A) and (B) of  FIG.  8    are a diagram illustrating that a bounding box of a first modification example input by a user is received to be acquired. 
       (A) and (B) of  FIG.  9    are a diagram illustrating an example in a case where a low-brightness region is extracted from a first region of a second modification example. 
         FIG.  10    is a diagram illustrating an example of a case where a ratio of a bounding box to an endoscopic image of a third modification example is equal to or larger than a certain threshold value. 
         FIG.  11    is a diagram illustrating an example of a case where a distance between a center of a bounding box of a fourth modification example and a center of an endoscopic image is equal to or less than a certain threshold value. 
       (A), (B), (C), and (D) of  FIG.  12    are a diagram illustrating a case where bounding boxes are set in new second regions excluding a low-brightness region of a fifth modification example. 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     First Embodiment 
     As shown in  FIG.  1   , a medical image processing system  10  is connected to an endoscope system  100 . The endoscope system  100  acquires endoscopic images that are obtained from the image pickup of an inside of a body, such as an alimentary canal. 
     The endoscope system  100  comprises a light source device  101 , an endoscope  102 , an endoscope processor device  103 , and a display  104 . The light source device  101  supplies illumination light, with which the inside of a subject is to be irradiated, to the endoscope  102 . The endoscope  102  irradiates a subject with at least one of light in a white-light wavelength range or light in a specific wavelength range and picks up images of the subject to acquire endoscopic images. The light in the specific wavelength range, which is used as the illumination light by the endoscope  102 , is, for example, light in a wavelength range shorter than a green-light wavelength range, particularly, light in a blue-light wavelength range or a violet-light wavelength range of a visible-light wavelength range. 
     The endoscope processor device  103  sequentially acquires the endoscopic images picked up by the endoscope  102 , and performs various types of image processing on the acquired endoscopic images. The endoscopic images subjected to the various types of image processing are displayed on the display  104 . Endoscopic images  50  (medical images) (see  FIG.  2   ), which are not yet subjected to or have been subjected to the various types of image processing, are transmitted to the medical image processing system  10  from the endoscope processor device  103 . 
     The endoscopic images  50  transmitted to the medical image processing system  10  from the endoscope processor device  103  are based on static images or a video that is picked up by the endoscope  102 . The video picked up by the endoscope  102  is formed of the plurality of endoscopic images  50  that are picked up in chronological order. The medical image processing system  10  can acquire frame images of the video as the endoscopic images  50 , which are static images, after an examination. 
     The medical image processing system  10  comprises a medical image processing device  11 , a display  12 , a storage device  13 , and an input device  14 . The display  12  is provided separately from the display  104  of the endoscope system, but the display  12  may be removed from the medical image processing system  10  and the display  104  may be used for both the medical image processing system  10  and the endoscope system. The input device  14  includes a keyboard (not shown), a mouse (not shown), a touch panel of the display  12 , and/or the like. 
     As shown in  FIG.  2   , the medical image processing device  11  acquires the endoscopic images  50  transmitted from the endoscope processor device  103  of the endoscope system  100 . The medical image processing device  11  comprises an image acquisition unit  15 , a bounding box acquisition unit  16 , an analysis unit  17 , an adjustment unit  18 , a storage controller  19 , and a display controller  21 . The image acquisition unit  15  sequentially acquires the endoscopic images  50  transmitted from the endoscope processor device  103 . 
     The medical image processing device  11  is formed of a well-known computer, and a program related to the various types of processing is incorporated in a program memory (not shown). The medical image processing device  11  is provided with a central controller (not shown) that is formed of a processor. The central controller executes the program incorporated in the program memory, so that the functions of the image acquisition unit  15 , the bounding box acquisition unit  16 , the analysis unit  17 , the adjustment unit  18 , the storage controller  19 , and the display controller  21  are realized. 
     As shown in (A) and (B) of  FIG.  3   , the bounding box acquisition unit  16  acquires a bounding box  51  from the endoscopic image  50  that is acquired by the image acquisition unit  15 . The bounding box  51  corresponds to a lesion area  52  (an object to be detected) shown in the endoscopic image  50 , and indicates a rectangular first region in which at least a part of the lesion area  52  is included. The lesion area  52  is, for example, a cancer, a polyp, or the like. 
     The endoscopic image  50  exemplified in this embodiment is obtained in a case where an image of a lumen in a body, such as a small intestine, a large intestine, an esophagus, or a blood vessel, having a dimension in a depth direction larger than a dimension in a radial direction, is picked up by an endoscope. Further, in a case where the image of such a lumen in a body is picked up, a crescent or doughnut-shaped lesion area shown in (A) and (B) of  FIG.  3    is likely to be present. 
     In this embodiment, the bounding box acquisition unit  16  calculates, for example, feature quantities of the endoscopic image  50  and includes a convolutional neural network (CNN) that performs processing for recognizing the lesion area  52  in the image. Accordingly, the bounding box acquisition unit  16  detects the lesion area  52  from the endoscopic image  50  (see (A) of  FIG.  3   ). 
     The bounding box acquisition unit  16  calculates a rectangular first region that is circumscribed about the lesion area  52  detected using CNN or the like, that is, the position, width, and height of the bounding box  51  from the endoscopic image  50  (see (B) of  FIG.  3   ). “A rectangle that is circumscribed” mentioned here is a rectangle that is circumscribed about the lesion area  52  and has two sides parallel to a horizontal axis (X axis) of the endoscopic image  50  and two sides parallel to a vertical axis (Y axis) of the endoscopic image  50 . 
     A configuration, which is used in a case where the bounding box acquisition unit  16  detects the lesion area  52  from the endoscopic image  50 , is not limited to the above-mentioned CNN, and the bounding box acquisition unit  16  may analyze feature quantities, such as a color, the gradient of pixel values, a shape, a size, and the like, of an image, using image processing to detect the lesion area  52 . 
     The analysis unit  17  analyzes the endoscopic image  50  to extract a contour of the lesion area  52 , acquires a second region (a region shown by hatching) that is a region inside this contour, and analyzes the brightness information of the first and second regions. Specifically, the analysis unit  17  calculates a ratio of the second region to the first region. The ratio of the second region to the first region means an area ratio. 
     The adjustment unit  18  adjusts the bounding box  51  in a case where a result of the analysis performed by the analysis unit  17  satisfies a certain condition. Specifically, the adjustment unit  18  adjusts the bounding box  51  in a case where a ratio of the second region to the first region is equal to or less than a certain threshold value. In a case where a ratio of the second region to the first region is equal to or less than the certain threshold value, a large portion of a region other than the lesion area  52  are included in the bounding box  51 . For this reason, the bounding box  51  in a case where a ratio of the second region to the first region is equal to or less than the certain threshold value is not suitable for machine learning. Accordingly, in order to correct this bounding box  51  and acquire a bounding box  51  suitable for machine learning, the adjustment unit  18  adjusts the bounding box  51 . The adjustment unit  18  does not adjust the bounding box  51  in a case where a ratio of the second region to the first region exceeds the certain threshold value. 
     A change in the position of the bounding box  51  in the endoscopic image  50  and a reduction in the area of the bounding box  51  are made as the adjustment of the bounding box  51  that is made by the adjustment unit  18 . Before adjusting the bounding box  51 , the adjustment unit  18  uses the brightness values of the first and second regions to calculate a coordinate position of a bounding box centroid GO 1  that is the centroid of the bounding box  51 , a coordinate position of a lesion area centroid GO 2  (object-to-be-detected centroid) that is the centroid of the lesion area  52 , a lesion area moment (object-to-be-detected moment) that is the moment of the lesion area  52  around the lesion area centroid GO 2 , and a bounding box moment that is the moment of the bounding box  51  around the lesion area centroid GO 2 . 
     First, the coordinate positions of the bounding box centroid GO 1  and the lesion area centroid GO 2  in the endoscopic image  50  are obtained from, for example, a well-known image moment calculation and are calculated from the following equation (1). f(i,j) is the brightness value of a pixel corresponding to coordinates (i,j). p and q are natural numbers including 0. 
     
       
         
           
             
               
                 
                   
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     m 00 , m 10 , and m 01  are calculated from Equation (1), and the coordinate position of a centroid G(i G ,j G ) is calculated from the following equation (2). 
     
       
         
           
             
               
                 
                   
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     In a case where the brightness information of the first and second regions are used in Equations (1) and (2) described above to make a calculation, the coordinate positions of the bounding box centroid GO 1  and the lesion area centroid GO 2  are calculated. Next, the adjustment unit  18  obtains a bounding box moment M 1 , which is a moment around the lesion area centroid GO 2 , and a lesion area moment M 2  around the lesion area centroid GO 2  from, for example, a well-known image moment calculation. A moment around a centroid is calculated from the following equation (3). 
     
       
         
           
             
               
                 
                   
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     As shown in (A) and (B) of  FIG.  4   , the adjustment unit  18  adjusts the bounding box  51 , that is, changes the position of the centroid of the bounding box  51  and a width W 1  and a height H 1  of the bounding box  51 . In a case where the adjustment unit  18  adjusts the bounding box  51 , the adjustment unit  18  uses the bounding box centroid GO 1 , the lesion area centroid GO 2 , the bounding box moment M 1 , and the lesion area moment M 2  described above. (A) of  FIG.  4    shows a bounding box that is not yet adjusted by the adjustment unit  18 , and (B) of  FIG.  4    shows an adjusted bounding box. 
     As the adjustment of the bounding box  51 , the adjustment unit  18  causes the bounding box centroid GO 1  to coincide with the lesion area centroid GO 2 , calculates a ratio M 1 /M 2  of the bounding box moment M 1  to the lesion area moment M 2 , and reduces the width W 1  and/or the height H 1  to a width W 2  and/or a height H 2  in a case where the ratio M 1 /M 2  exceeds a certain threshold value (a state shown in (B) of  FIG.  4   ). In (B) of  FIG.  4   , a white circle indicates the bounding box centroid GO 1  before adjustment and a black circle indicates the bounding box centroid GO 1  after adjustment. 
     Then, the adjustment unit  18  calculates a bounding box moment M 1  and a ratio M 1 /M 2  again, and determines the width W 2  and the height H 2  in a case where the ratio M 1 /M 2  is equal to or less than a certain threshold value. A condition in which the brightness value of the lesion area  52  is larger than that of a region other than the lesion area  52  in the endoscopic image  50  is considered as a premise of the calculation of the ratio M 1 /M 2  and the determination of the width W 2  and the height H 2 . For this reason, in a case where the bounding box centroid GO 1  is aligned with the lesion area centroid GO 2 , the value of an image moment, which is a value to which the brightness value is added, is increased around the lesion area centroid GO 2 . However, as a region is away from the lesion area centroid GO 2 , the brightness value of the region is reduced and the value of an image moment of the region is reduced (the region does not include the lesion area  52 ). Accordingly, in order to remove a region of which the value of an image moment is small (a region not including the lesion area  52 ), the adjustment unit  18  reduces the width W 1  and/or the height H 1  of the bounding box  51  and reduces the bounding box moment M 1 . As a result, the above-mentioned ratio M 1 /M 2  is reduced. Further, in a case where the ratio M 1 /M 2  is equal to or less than the certain threshold value, a region other than the lesion area  52  is reduced in the bounding box  51 . As a result, data suitable for machine learning are obtained. In a case where the adjustment unit  18  makes an adjustment, the adjustment unit  18  may reduce the width and height of the bounding box  51  and repeatedly recalculate a bounding box moment M 1  and a ratio M 1 /M 2  until the ratio M 1 /M 2  is equal to or less than the certain threshold value. 
     The adjustment unit  18  makes an adjustment as described above, so that the position of the adjusted bounding box  51  is changed to a position where the bounding box centroid GO 1  is the lesion area centroid GO 2  and the area of the adjusted bounding box  51  is reduced as compared to the area of the bounding box  51  not yet adjusted. The endoscopic image  50  and the adjusted bounding box  51  are input to the storage controller  19 . 
     The storage controller  19  associates a new adjusted bounding box  51  with the endoscopic image  50 . In addition, the storage controller  19  stores the endoscopic image  50  and the new adjusted bounding box  51 , which is associated with the endoscopic image  50 , in the storage device  13 . In this case, it is preferable that the bounding box  51  is added to the endoscopic image  50  as accessory information of the endoscopic image  50 . Alternatively, the endoscopic image  50  and the bounding box  51  may be separately stored, and information in which the endoscopic image  50  and the bounding box  51  are linked to each other may be added to each of the endoscopic image  50  and the bounding box  51  and are stored. 
     The storage device  13  is a hard disk drive, a solid-state drive, and/or the like built in the medical image processing device  11 . The storage device  13  is not limited thereto, and an external storage device connected to the medical image processing device  11  via a cable or a network, a cloud service in which data are stored in a server connected to the medical image processing device  11  via the Internet, or the like may be used as the storage device  13 . In a case where the medical image processing device  11  is to perform various types of processing, the display controller  21  causes the display  12  to display a setting screen on which a user performs a setting operation. 
     A series of flows in which the medical image processing device  11  acquires and adjusts the bounding box  51 , associates the bounding box  51  with the endoscopic image  50 , and stores the bounding box  51  and the endoscopic image  50  will be described with reference to a flowchart shown in  FIG.  5   . The image acquisition unit  15  sequentially acquires the endoscopic images  50  from the endoscope processor device  103  (S 101 ). The bounding box acquisition unit  16  acquires the bounding box  51 , which indicates the rectangular first region in which at least a part of the lesion area  52  is included, from the endoscopic image  50  (S 102 ). Further, the analysis unit  17  acquires the second region that is a region inside the contour of the lesion area  52 , and analyzes the brightness information of the first and second regions (S 103 ). 
     The adjustment unit  18  compares a ratio of the second region to the first region with a certain threshold value (S 104 ). Then, in a case where the ratio of the second region to the first region is equal to or less than the certain threshold value (Y in S 104 ), the adjustment unit  18  adjusts the bounding box  51  (S 105 ). In a case where the ratio of the second region to the first region exceeds the certain threshold value (N in S 104 ), the adjustment unit  18  does not adjust the bounding box  51 . In this case, the storage controller  19  stores the endoscopic image  50  and an unadjusted bounding box  51  associated with the endoscopic image  50  (S 108 ). 
     As described above, as the adjustment of the bounding box  51 , the adjustment unit  18  causes the centroid of the bounding box  51  to coincide with the centroid of the lesion area  52  and reduces the width and/or the height of the bounding box  51 . In a case where the ratio M 1 /M 2  between the moments is equal to or less than a certain threshold value (Yin S 106 ) after the bounding box  51  is adjusted, the adjustment unit  18  determines the width and the height of the bounding box  51  (S 107 ). On the other hand, in a case where the ratio M 1 /M 2  between the moments exceeds the certain threshold value (N in S 106 ), the adjustment unit  18  reduces the width and the height of the bounding box  51  again and calculates a bounding box moment M 1  and a ratio M 1 /M 2  (S 105 ). Then, in a case where the ratio M 1 /M 2  is equal to or less than the certain threshold value, the adjustment unit  18  determines the width and the height of the bounding box  51  as described above (S 107 ). 
     In a case where the adjustment unit  18  adjusts the bounding box  51  and determines the width and the height of the bounding box  51 , the storage controller  19  stores the endoscopic image  50  and a new adjusted bounding box  51  associated with the endoscopic image  50  (S 108 ). The stored endoscopic image  50  and the stored bounding box  51  are used as correct answer data for machine learning. 
     Since an adjustment for changing the position of the bounding box  51  and reducing the area of the bounding box  51  is made in the medical image processing device  11  as described above, a region other than the lesion area  52  is reduced in the bounding box  51 . As a result, correct answer data suitable for machine learning are obtained. 
     Further, in a case where the adjustment unit  18  adjusts the bounding box  51 , the adjustment unit  18  uses the bounding box centroid GO 1 , the lesion area centroid GO 2 , the bounding box moment M 1 , and the lesion area moment M 2  to change the centroid of the bounding box  51  and the width and the height of the bounding box  51 . Accordingly, the adjustment unit  18  can adjust the bounding box  51  with a high accuracy, so that correct answer data more suitable for machine learning are obtained. 
     Second Embodiment 
     An endoscopic image and a new adjusted bounding box associated with the endoscopic image are stored in the storage device in the first embodiment, but the present invention is not limited thereto. An endoscopic image and a new adjusted bounding box associated with the endoscopic image may be input to a learning unit that performs machine learning. In the following description, the same components as those of the medical image processing device  11  according to the first embodiment will be denoted by the same reference numerals as those of the medical image processing device  11  and the description thereof will be omitted. 
     As shown in  FIG.  6   , a medical image processing device  61  includes a learning unit  62 . The learning unit  62  corresponds to a learning device of claims. The medical image processing device  61  is provided with the same central controller as that of the medical image processing device  11  according to the first embodiment, and the central controller executes a program, so that the functions of the learning unit  62  in addition to an image acquisition unit  15 , a bounding box acquisition unit  16 , an analysis unit  17 , an adjustment unit  18 , a storage controller  19 , and a display controller  21  are realized. 
     In the medical image processing device  61  according to this embodiment, an endoscopic image  50  and a new adjusted bounding box  51  associated with the endoscopic image  50  are input to the learning unit  62  and the learning unit  62  performs machine learning for a lesion area (an object to be detected). A configuration and a series of flows from the acquisition of the endoscopic image  50  up to the adjustment of the bounding box  51  are the same as those of the first embodiment. 
     Further, the endoscopic image  50  and the new adjusted bounding box  51  associated with the endoscopic image  50  may be input to and stored in the storage device  13  as in the first embodiment, and may be input to only the learning unit  62  and may not be input to the storage device  13 . 
     A flow in which the medical image processing device  61  acquires and adjusts the bounding box  51 , associates the bounding box  51  with the endoscopic image  50 , and stores the bounding box  51  and the endoscopic image  50  will be described with reference to a flowchart shown in  FIG.  7   . The acquisition of the endoscopic image  50  (S 201 ), the acquisition of the bounding box  51  (S 202 ), the analysis of the first region and the second region (S 203  and S 204 ), and the adjustment of the bounding box  51  (S 205  to S 207 ) are the same processing as S 101  to S 107  of the flowchart of the first embodiment. 
     In a case where the bounding box  51  is adjusted and the width and the height of the bounding box  51  are determined (S 207 ), the adjustment unit  18  inputs the endoscopic image  50  and a new adjusted bounding box  51  associated with the endoscopic image  50  to the learning unit  62  (S 208 ). The learning unit  62  performs machine learning for a lesion area (an object to be detected) using the endoscopic image  50  and the bounding box  51  (S 209 ). 
     Since an adjustment for changing the position of the bounding box  51  and reducing the area of the bounding box  51  is made in the medical image processing device  61  as described above, a region other than the lesion area  52  is reduced in the bounding box  51 . As a result, correct answer data suitable for machine learning are obtained. That is, the same effects as the first embodiment can be obtained. In addition, since the endoscopic image  50  and the bounding box  51  are input to the learning unit  62  as the correct answer data, a discriminator, which can detect a lesion area with a high accuracy and can discriminate the type of a lesion, can be generated in a case where the learning unit  62  performs machine learning. 
     First Modification Example 
     In each embodiment, in a step where the bounding box acquisition unit  16  acquires a bounding box not yet adjusted, a lesion area, which is an object to be detected, is detected by machine learning, such as CNN, and a bounding box circumscribed about the lesion area is acquired. However, the present invention is not limited thereto and, as shown in (A) and (B) of  FIG.  8   , the bounding box acquisition unit  16  may receive a bounding box, which is input to an endoscopic image  50  displayed on the display  12  by a medical doctor who is a user, to acquire a bounding box that is not yet adjusted. 
     In an example shown in (A) and (B) of  FIG.  8   , an input from a user is made in a case where a user designates a first point P 1  positioned on the upper left side of the bounding box  51  (see (A) of  FIG.  8   ) and designates a second point P 2  that is positioned on the lower right side and is diagonal to the first point of the bounding box  51  (see (B) of  FIG.  8   ). The first and second points P 1  and P 2  are designated by, for example, the operation of the mouse of the input device  14 , the operation of a finger on the touch panel, or the like. Then, the bounding box acquisition unit  16  receives the bounding box  51  that is input by the user in this way. A flow after the acquisition of the bounding box  51  is the same as that of each embodiment. 
     The bounding box  51  is acquired from the endoscopic image  50  by the medical image processing devices  11  and  61  or the bounding box  51  is input by a user in the respective embodiments and the modification example, but the present invention is not limited thereto. Before the medical image processing devices  11  and  61  acquire the endoscopic image  50 , for example, a bounding box  51  acquired in advance by the endoscope processor device  103  or the like may be acquired. Likewise, before the medical image processing devices  11  and  61  acquire a lesion area  52  (an object to be detected), a lesion area  52  shown in the endoscopic image  50  acquired in advance may be acquired as the second region. In these cases, it is preferable that the bounding box  51  and/or the lesion area  52  acquired in advance is input to the medical image processing devices  11  and  61  in a state where the bounding box  51  and/or the lesion area  52  acquired in advance are associated with the endoscopic image  50 . It is preferable that the first region is a region detected as a rectangular region using an object detection method or the like. Further, it is preferable that the second region is a region obtained in a case where the lesion area  52  is detected in a shape different from a rectangular region using a segmentation method, contour extraction, or the like. Since the information of the second region is used in addition to information of the first region, the bounding box can be reduced in size to fit the size of the lesion area  52  better. 
     Second Modification Example 
     In each embodiment, the analysis unit  17  performs an analysis and a condition in which a ratio of the second region to the first region is equal to or less than a certain threshold value is exemplified as a certain condition in a case where the adjustment unit  18  adjusts the bounding box  51 . However, the present invention is not limited thereto. As shown in (A) and (B) of  FIG.  9   , a low-brightness region having a brightness value equal to or less than a certain value in a first region is extracted and a condition in which a ratio of the low-brightness region to the first region is equal to or larger than a certain threshold value may be used as the above-mentioned certain condition. 
     As shown in (A) of  FIG.  9   , the analysis unit  17  analyzes a bounding box  51  that is acquired from the endoscopic image  50  and is not yet adjusted. After that, the analysis unit  17  extracts a low-brightness region LA 1  (a region shown by cross-hatching) having a brightness value equal to or less than a certain value in the bounding box  51 , that is, in the first region as shown in (B) of  FIG.  9   , and obtains a ratio of the low-brightness region LA 1  to the first region. The ratio mentioned here means an area ratio of the low-brightness region LA 1  to the first region. Then, the adjustment unit  18  adjusts the bounding box  51  in a case where the ratio of the low-brightness region LA 1  to the first region is equal to or larger than a certain threshold value. 
     As described above, a lesion area  52  has a brightness value larger than the brightness value of a region other than the lesion area  52  in the endoscopic image  50 . Accordingly, there is a high possibility that the low-brightness region LA 1  is a region other than the lesion area  52 , and the bounding box  51  needs to be adjusted in a case where a large portion of the low-brightness region LA 1  is included in the bounding box  51 . A flow after the adjustment of the bounding box  51  is the same as that of each embodiment. 
     Third Modification Example 
     Further, the certain condition in a case where the adjustment unit  18  adjusts the bounding box  51  is not limited to the conditions described in each embodiment and the second modification example. A ratio of a bounding box to an endoscopic image may be calculated and a condition in which this ratio is equal to or larger than a certain threshold value may be used as the above-mentioned certain condition.  FIG.  10    is a diagram illustrating an example of a case where a ratio of a bounding box  51  to an endoscopic image  50  is equal to or larger than a certain threshold value. 
     In  FIG.  10   , the bounding box  51  has an area close to the area of the endoscopic image  50 . That is, a ratio of the bounding box  51  to the endoscopic image  50  is large and is equal to or larger than a certain threshold value. Since a large portion of a region other than the lesion area  52  is included in the bounding box  51  in a case where this ratio is large, the bounding box  51  is not suitable for machine learning. Accordingly, the bounding box  51  needs to be adjusted. A flow after the adjustment of the bounding box  51  is the same as that of each embodiment. 
     Fourth Modification Example 
     Further, a condition in which a distance between a center or a centroid of a bounding box and a center of an endoscopic image is equal to or less than a certain threshold value may be used as the certain condition in a case where the adjustment unit  18  adjusts the bounding box  51 .  FIG.  11    is a diagram illustrating an example of a case where a distance between a center of a bounding box  51  and a center of an endoscopic image  50  is equal to or less than a certain threshold value. 
     In  FIG.  11   , a center O 1  of the bounding box  51  and a center O 2  of the endoscopic image  50  are present at positions close to each other. That is, a distance D between the center O 1  and the center O 2  is equal to or less than a certain threshold value. Since a large portion of a region other than the lesion area  52  is included in the bounding box  51  in a case where this distance D is short, the bounding box  51  is not suitable for machine learning. Accordingly, the bounding box  51  needs to be adjusted. A flow after the adjustment of the bounding box  51  is the same as that of each embodiment. Further, a condition in which the distance D between the center of the bounding box  51  and the center of the endoscopic image  50  is equal to or less than the certain threshold value is used as the certain condition in an example shown in  FIG.  11   , but the present invention is not limited thereto. A condition in which the distance D between the centroid of the bounding box  51  and the center of the endoscopic image  50  is equal to or less than a certain threshold value may be used as the certain condition. 
     Fifth Modification Example 
     In each embodiment, as the adjustment of the bounding box, the centroid and the moment of the lesion area and the centroid and the moment of the bounding box are calculated, and the centroid, the width, and the height of the bounding box are changed in a case where the ratio between the moments exceeds a certain threshold value. However, the present invention is not limited thereto and, as shown in (A), (B), (C), and (D) of  FIG.  12   , a low-brightness region LA 2  having a brightness value equal to or less than a certain value in a bounding box  51  may be excluded and new bounding boxes  51  circumscribed about new second regions of the endoscopic image  50  excluding the low-brightness region LA 2  may be calculated. 
     As shown in (A) of  FIG.  12   , the adjustment unit  18  acquires the brightness information of a bounding box  51  in an endoscopic image  50 . As shown in (B) of  FIG.  12   , the adjustment unit  18  extracts a low-brightness region LA 2  (a region shown by cross-hatching) having a brightness value equal to or less than a certain value in the bounding box  51 . A flow from the acquisition of the endoscopic image  50  up to the acquisition and analysis of a bounding box  51 , which is not yet adjusted, is the same as that of each embodiment. 
     As shown in (C) of  FIG.  12   , the adjustment unit  18  then excludes the low-brightness region LA 2  from the bounding box  51 . Since the low-brightness region LA 2  is excluded from the bounding box  51 , a part of a lesion area  52  is cut off and second regions (regions shown by hatching) are formed. Then, the adjustment unit  18  calculates new bounding boxes circumscribed about the new second regions of the endoscopic image  50  excluding the low-brightness region LA 2 . 
     Since the low-brightness region LA 2  is excluded in an example shown in (C) of  FIG.  12   , a state where a region inside the contour of the lesion area  52  is divided into two second regions  52 A and  52 B is made. For this reason, the adjustment unit  18  calculates new bounding boxes  51 A and  51 B that are circumscribed about the divided second regions  52 A and  52 B, respectively. Since the area of the second regions  52 A and  52 B from which the low-brightness region LA 2  is excluded is smaller than the area of the lesion area  52  not yet adjusted, the widths and/or the heights of the bounding boxes  51 A and  51 B circumscribed about the second regions  52 A and  52 B are also reduced. Accordingly, a region other than a lesion area  52  is reduced in the bounding boxes  51 A and  51 B, so that data suitable for machine learning are obtained. 
     In this modification example, well-known sharpness processing (contour enhancement processing) may be performed on the endoscopic image  50 . Accordingly, since the contours of the second regions  52 A and  52 B are enhanced and blurred portions of boundaries are removed, the adjusted second regions  52 A and  52 B are surely smaller than the lesion area  52  not yet adjusted and the bounding boxes  51 A and  51 B circumscribed about the adjusted second regions  52 A and  52 B are also reduced in size. 
     Then, as shown in (D) of  FIG.  12   , the adjustment unit  18  associates the new adjusted bounding boxes  51 A and  51 B with the endoscopic image  50  and stores the endoscopic image  50  and the new adjusted bounding boxes  51 A and  51 B associated with the endoscopic image  50  as in the first embodiment. Alternatively, the adjustment unit  18  inputs the endoscopic image  50  and the new adjusted bounding box  51  associated with the endoscopic image  50  to the learning unit  62  as in the second embodiment. Accordingly, the same effects as the each embodiment can be obtained. 
     The new bounding boxes circumscribed about the new second regions of the endoscopic image  50  excluding the low-brightness region LA 2  are calculated in the fifth modification example, but the present invention is not limited thereto. After the bounding boxes  51 A and  51 B circumscribed about the new second regions  52 A and  52 B are calculated, the same adjustment as the first and second embodiments may be further made. In this case, the adjustment unit  18  uses the brightness values of the first and second regions excluding the low-brightness region LA 2  to calculate bounding box centroids that are the centroids of the bounding boxes  51 A and  51 B, lesion area centroids that are the centroids of the lesion areas (the second regions  52 A and  52 B), the lesion area moments of the lesion areas (the second regions  52 A and  52 B), and the bounding box moments of the bounding boxes  51 A and  51 B. 
     Then, the adjustment unit  18  uses the bounding box centroids, the lesion area centroids, the bounding box moments, and the lesion area moments to change the positions of the centroids of the bounding boxes  51 A and  51 B and the widths and the heights of the bounding boxes  51 A and  51 B as in the first and second embodiments. Accordingly, the bounding boxes  51 A and  51 B are adjusted with a higher accuracy, so that correct answer data more suitable for machine learning are obtained. 
     Further, an example in which a region inside the contour of the lesion area  52  is divided into two second regions  52 A and  52 B since the low-brightness region LA 2  is excluded is described in the fifth modification example, but the present invention is not limited thereto. In a case where a low-brightness region LA 2  of a bounding box  51  is removed to form a new second region having an area smaller than the area of the original second region and a bounding box is adjusted to correspond to the new second region, the same effects as the fifth modification example can be obtained. 
     With regard to the endoscope system  100 , a capsule endoscope may be used as the endoscope  102 . In this case, the light source device  101  and a part of the endoscope processor device  103  can be mounted on the capsule endoscope. 
     Further, the medical image is not limited to an endoscopic image described in each of the embodiments and the modification examples, but it is preferable that the medical image is a medical image obtained in a case where an image of a lumen in a body having a dimension in a depth direction larger than a dimension in a radial direction is picked up. Furthermore, in each of the embodiments and the modification examples, the analysis unit  17  and the adjustment unit  18  analyze the brightness information and the brightness values of the first and second regions and adjust the bounding box  51  using the results of the analysis. However, the present invention is not limited thereto, and the analysis unit  17  and the adjustment unit  18  may make an analysis and adjust the bounding box  51  using the pixel information and the pixel values of the first and second regions. 
     In each of the embodiments and the modification examples, the hardware structures of processing units, which perform various types of processing, such as the image acquisition unit  15 , the bounding box acquisition unit  16 , the analysis unit  17 , the adjustment unit  18 , the storage controller  19 , the display controller  21 , and the learning unit  62 , are various processors to be described below. Various processors include: a central processing unit (CPU) that is a general-purpose processor functioning as various processing units by executing software (program); a programmable logic device (PLD) that is a processor of which the circuit configuration can be changed after manufacture, such as a field programmable gate array (FPGA); a dedicated electrical circuit that is a processor having circuit configuration designed exclusively to perform various types of processing; a graphical processing unit (GPU) that performs a large amount of processing, such as image processing, in parallel; and the like. 
     One processing unit may be formed of one of these various processors, or may be formed of a combination of two or more processors of the same type or different types (for example, a plurality of FPGAs, a combination of a CPU and an FPGA, or a combination of a CPU and a GPU). Further, a plurality of processing units may be formed of one processor. As an example where a plurality of processing units are formed of one processor, first, there is an aspect where one processor is formed of a combination of one or more CPUs and software as typified by a computer, such as a client or a server, and functions as a plurality of processing units. Second, there is an aspect where a processor fulfilling the functions of the entire system, which includes a plurality of processing units, by one integrated circuit (IC) chip as typified by System On Chip (SoC) or the like is used. In this way, various processing units are formed using one or more of the above-mentioned various processors as hardware structures. 
     In addition, the hardware structures of these various processors are more specifically electrical circuitry where circuit elements, such as semiconductor elements, are combined. 
     EXPLANATION OF REFERENCES 
     
         
         
           
               10 : medical image processing system 
               11 : medical image processing device 
               12 : display 
               13 : storage device 
               14 : input device 
               15 : image acquisition unit 
               16 : bounding box acquisition unit 
               17 : analysis unit 
               18 : adjustment unit 
               19 : storage controller 
               21 : display controller 
               50 : endoscopic image 
               51 : bounding box 
               51 A,  51 B: bounding box 
               52 : lesion area 
               52 A,  52 B: second region 
               61 : medical image processing device 
               62 : learning unit 
               100 : endoscope system 
               101 : light source device 
               102 : endoscope 
               103 : endoscope processor device 
               104 : display 
             D: distance 
             G: centroid 
             GO 1 : bounding box centroid 
             GO 2 : lesion area centroid 
             H 1 , H 2 : height 
             LA 1 , LA 2 : low-brightness region 
             M 1 : bounding box moment 
             M 2 : lesion area moment 
             O 1 , O 2 : center 
             P 1 : first point 
             P 2 : second point 
             W 1 , W 2 : width