Medical imaging apparatus and method of operation thereof

Abstract

To provide a medical image processing apparatus that allows for efficiently selecting a medical image suitable for machine learning out of a medical moving image, and an operating method of the apparatus.SOLUTION: A medical image processing apparatus 11 comprises: an image acquisition unit 15 that acquires a medical moving image; a luminance analysis unit 16 that respectively analyses luminance information on a plurality of specific medical images within a specific time range, among a plurality of medical images constituting the medical moving image, and outputs luminance analysis information; and an image selection unit 17 that selects a learning medical image to use for machine learning from among the specific medical images by using the luminance analysis information.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to medical imaging apparatus and methods of operation. [Background technology]

[0002] In the medical field, image diagnosis such as diagnosis of a patient's condition and follow-up observation is performed using medical images such as endoscopic images, X-ray images, CT (Computed Tomography) images, and MR (Magnetic Resonance) images. Doctors and others make decisions on treatment plans based on such image diagnosis.

[0003] In recent years, machine learning such as deep learning has become widespread in image diagnosis using medical images. Patent Document 1 describes a medical image processing device that acquires a medical video consisting of a plurality of medical images and report information corresponding to the medical video, analyzes the report information, and stores medical images including lesion images in a storage device. The medical images stored in the storage device are used for machine learning.

[0004] On the other hand, as an image diagnostic system using machine learning, a computer-aided image diagnostic system (CAD: Computer Aided Diagnosis, hereafter referred to as CAD) is known, which analyzes medical images such as endoscopic images to automatically detect regions of interest such as lesions, and highlights the detected regions of interest. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] International Publication No. 2019 / 198637 Summary of the Invention [Problem to be solved by the invention]

[0006] The development of CAD using machine learning requires the acquisition of a large number of medical images. Therefore, it is being considered to perform machine learning using medical videos taken during examinations. Medical videos contain multiple medical images taken in chronological order.

[0007] However, medical videos include low-quality medical images captured in a state where the medical images are blurred, shaken, or covered with water, or in close contact with mucous membranes. These images are not suitable for machine learning for developing CAD. Therefore, in order to perform machine learning using medical videos, it is necessary to exclude medical videos with low image quality. However, the medical image processing device described in the above Patent Document 1 does not have a configuration for excluding medical images with low image quality from medical videos. For this reason, multiple medical images are displayed on a display or the like, and the user is required to visually check and exclude the medical images with low image quality, which is time-consuming and laborious.

[0008] The present invention aims to provide a medical image processing device and an operating method thereof that can efficiently select medical images suitable for machine learning from medical videos. [Means for solving the problem]

[0009] The medical image processing device of the present invention is equipped with a processor, which acquires a medical video, analyzes the luminance information of a number of specific medical images that fall within a specific time range among a number of medical images that constitute the medical video, outputs luminance analysis information, and uses the luminance analysis information to select a learning medical image to be used for machine learning from the number of specific medical images.

[0010] It is preferable that the processor outputs size information of areas having a brightness above a certain threshold for each of the multiple specific medical images as brightness analysis information, compares the size information of the multiple specific medical images, and selects a learning medical image to be used for machine learning from the multiple specific medical images.

[0011] It is preferable that the processor outputs size information and position information of areas having a brightness above a certain threshold for each of the multiple specific medical images as brightness analysis information, compares the size information and position information of the multiple specific medical images, and selects a training medical image to be used for machine learning from the multiple specific medical images.

[0012] It is preferable that the processor calculates size information for a target medical image and surrounding medical images different from the target medical image among the multiple medical images that make up the medical video, calculates the amount of change in the size information between the target medical image and the surrounding medical images, and uses the amount of change to decide whether or not to select the target medical image as a learning medical image.

[0013] It is preferable that the processor calculates size information for a target medical image and multiple surrounding medical images different from the target medical image among the multiple medical images that make up the medical video, calculates a difference in the amount of change using the size information between the target medical image and the multiple surrounding medical images, and uses the difference to decide whether or not to select the target medical image as a learning medical image.

[0014] It is preferable that the processor calculates a first pixel number, which is the number of pixels in areas having a brightness above a certain threshold, for a target medical image among the multiple specific medical images, calculates a second pixel number, which is the number of pixels in areas having a brightness above a certain threshold, for surrounding medical images among the multiple specific medical images that are different from the target medical image, output the first pixel number and the second pixel number as brightness analysis information, and determine whether or not to select the target medical image as a learning medical image by comparing the first pixel number and the second pixel number.

[0015] Preferably, the processor excludes the target medical image from the training medical images if the first pixel count is greater than the maximum, average or median of the second pixel count.

[0016] It is preferable that the processor divides a target medical image among the multiple specific medical images into multiple regions, outputs divided region luminance analysis information, which is size information of regions having a luminance above a certain threshold, as luminance analysis information for each of the multiple regions of the target medical image, calculates surrounding luminance analysis information, which is size information of regions having a luminance above a certain threshold, for surrounding medical images different from the target medical image, and compares the divided region luminance analysis information with the surrounding luminance analysis information to select a learning medical image to be used for machine learning from the multiple specific medical images.

[0017] It is preferable that the processor divides a target medical image among the multiple specific medical images into multiple regions, outputs divided region luminance analysis information, which is size information and position information of regions having a luminance above a certain threshold, as luminance analysis information for each of the multiple regions of the target medical image, calculates surrounding luminance analysis information, which is size information and position information of regions having a luminance above a certain threshold, for surrounding medical images different from the target medical image, and compares the divided region luminance analysis information with the surrounding luminance analysis information to select a learning medical image to be used for machine learning from the multiple specific medical images.

[0018] It is preferable that the processor divides a target medical image among the multiple specific medical images into multiple regions, calculates a second pixel number, which is the number of pixels in areas having a brightness above a certain threshold, for surrounding medical images among the multiple specific medical images that are different from the target medical image, calculates a third pixel number, which is the number of pixels in areas having a brightness above a certain threshold, for each of the multiple regions, output the second pixel number and the third pixel number as brightness analysis information, compares the second pixel number with the third pixel number in at least one of the multiple regions to extract an area within the multiple regions having a brightness above a certain threshold as a target region, and determines whether to select the target medical image as a learning medical image by comparing the target medical image with the target region.

[0019] If the third number of pixels is greater than the maximum, average, or median of the second number of pixels, the processor preferably determines, as the target region, a region within the multiple regions corresponding to the third number of pixels that has a brightness equal to or greater than a certain threshold.

[0020] It is preferable that the processor excludes the target medical image from the training medical images if the total number of pixels in the target region is equal to or greater than a certain percentage of the number of pixels in the target medical image.

[0021] Preferably, if the maximum, average or median number of pixels in the target region is equal to or greater than a certain percentage of the number of pixels in the target medical image, the processor excludes the target medical image from the training medical images.

[0022] It is preferable that the processor determines a certain threshold value used when outputting the brightness analysis information according to operation information, imaging conditions, or an imaging device when capturing a medical image.

[0023] Preferably, the processor analyzes the intensity analysis information from three or more particular medical images to select a training medical image.

[0024] The processor preferably stores the training medical images in the storage device. The processor preferably performs machine learning using the selected training medical images.

[0025] The operating method of the medical image processing device of the present invention includes the steps of acquiring a medical video, analyzing the luminance information of a number of specific medical images within a specific time range among a number of medical images that constitute the medical video, and outputting luminance analysis information, and using the luminance analysis information to select a learning medical image to be used for machine learning from the number of specific medical images. Effect of the Invention

[0026] According to the present invention, medical images suitable for machine learning can be efficiently selected from medical videos. [Brief description of the drawings]

[0027] [Figure 1] 1 is a block diagram showing functions of a medical image processing system and an endoscope system. [Diagram 2] FIG. 2 is a block diagram showing the functions of the medical image processing apparatus. [Diagram 3] FIG. 11 is an explanatory diagram showing a luminance analysis process and a selection process. [Figure 4] 4 is a flowchart relating to selection of a learning endoscopic image in the first embodiment. [Diagram 5] 13 is an explanatory diagram showing a luminance analysis process and a selection process in the second embodiment. FIG. [Figure 6] 13 is an explanatory diagram showing size information calculated in the luminance analysis process in the second embodiment. FIG. [Figure 7] 13 is a flowchart relating to the selection of learning endoscopic images in the second embodiment. [Figure 8] FIG. 11 is an explanatory diagram showing position information calculated in the luminance analysis process in the first modified example. [Figure 9] FIG. 13 is an explanatory diagram showing a luminance analysis process and a selection process in the third embodiment. [Figure 10] 13 is an explanatory diagram showing the selection process in the third embodiment, which illustrates the process of calculating the ratio of the total number of pixels in a target region to the number of pixels in an endoscopic image. FIG. [Figure 11] 13 is a flowchart relating to the selection of learning endoscopic images in the third embodiment. [Figure 12] FIG. 11 is a block diagram showing functions of a medical image processing apparatus in a second modified example. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0028] [First embodiment] 1, a medical image processing system 10 is connected to an endoscope system 100. The endoscope system 100 acquires an endoscopic video obtained by capturing images of the inside of a body such as the digestive tract.

[0029] The endoscope system 100 includes 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 to the endoscope 102 for irradiating the inside of a subject. The endoscope 102 captures an endoscopic video by irradiating at least one of light in a white wavelength band and light in a specific wavelength band and capturing an image of the subject. The light in the specific wavelength band used by the endoscope 102 for illumination light is, for example, light in a wavelength band shorter than the green wavelength band, particularly light in the blue or violet band of the visible range.

[0030] The endoscope processor device 103 sequentially acquires endoscopic moving images captured by the endoscope 102 and performs various image processing on the acquired endoscopic moving images. The endoscopic moving images that have been subjected to various image processing are displayed on the display 104. The endoscopic moving images before or after the various image processing are transmitted from the endoscope processor device 103 to the medical image processing system 10.

[0031] The endoscopic video 50 (medical video) (see FIG. 2) transmitted from the endoscope processor device 103 to the medical image processing system 10 is composed of a plurality of endoscopic images 51 (medical images) (see FIG. 3) captured in chronological order by the endoscope 102, and the frame images constituting the endoscopic video 50 after the examination can be acquired as still endoscopic images 51. The endoscopic video 50 includes endoscopic videos captured by a doctor operating the endoscope 102, etc., as well as endoscopic videos captured automatically without the doctor's instructions.

[0032] The medical image processing system 10 includes a medical image processing device 11, a display 12, and a storage device 13. The display 12 is provided separately from the display 104 of the endoscope system, but in the medical image processing system 10, the display 12 may be eliminated and the display 104 may be used as the storage device.

[0033] 2, the medical image processing device 11 acquires an endoscopic video 50 transmitted from the endoscope system 100. The medical image processing device 11 includes an image acquisition unit 15, a brightness analysis unit 16, an image selection unit 17, a storage control unit 18, and a display control unit 19. The image acquisition unit 15 sequentially acquires the endoscopic video 50 transmitted from the endoscope processor device 103 of the endoscope system 100.

[0034] The medical image processing device 11 is composed of a well-known computer, and programs related to various processes are incorporated in a program memory (not shown). The medical image processing device 11 is provided with a central control unit (not shown) composed of a processor. The central control unit executes the programs in the program memory to realize the functions of an image acquisition unit 15, a brightness analysis unit 16, an image selection unit 17, a storage control unit 18, and a display control unit 19.

[0035] The luminance analysis unit 16 analyzes the luminance information of each of a plurality of specific endoscopic images (specific medical images) within a specific time range among a plurality of endoscopic images constituting the endoscopic video 50, and outputs the luminance analysis information. The luminance analysis information output by the luminance analysis unit 16 will be described later.

[0036] The image selection unit 17 selects a learning endoscopic image 52 (learning medical image) to be used for machine learning from a plurality of specific endoscopic images using the luminance analysis information output by the luminance analysis unit 16. The memory control unit 18 stores the learning endoscopic image 52 selected by the image selection unit 17 in the storage device 13. The display control unit 19 causes the display 12 to display a setting screen or the like on which a user performs setting operations when the medical image processing device 11 executes various processes.

[0037] The storage device 13 is a hard disk drive built into the medical image processing device 11 or connected via a cable or a network, or a disk array in which a plurality of hard disk drives are connected in series.

[0038] The analysis process in which the luminance analysis unit 16 outputs luminance analysis information will be described. As shown in FIG. 3(A), the luminance analysis unit 16 analyzes the luminance information of a plurality of specific endoscopic images (specific medical images) within a specific time range TR among a plurality of endoscopic images 51 constituting an endoscopic video 50. The specific time range TR may be, for example, the entire time range when the endoscopic video 50 is captured, or may be a time range excluding a certain time after the start of the examination and before the end of the examination from the time range when the endoscopic video 50 is captured. The specific time range TR is preferably determined as a time range in which the endoscopic imaging position does not change significantly, and is preferably set to 15 seconds, and more preferably set to 5 seconds. The specific time range TR may also be determined as a time range based on the recognition result of the imaging scene of the endoscopic image.

[0039] As shown in Fig. 3(B), the luminance analysis unit 16 analyzes luminance information for a target endoscopic image 51S (target medical image) and surrounding endoscopic images 51A and 51B (surrounding medical images) different from the target endoscopic image 51S as a plurality of specific endoscopic images within a specific time range TR. In the example shown in Fig. 3(B), the luminance analysis unit 16 sets the endoscopic images 51, which are frame images captured immediately before and after the target endoscopic image 51S, as surrounding endoscopic images 51A and 51B among the plurality of endoscopic images 51 arranged in chronological order.

[0040] In the example shown in FIG. 3(A) and FIG. 3(B), a low-image-quality region having a luminance equal to or greater than a certain threshold is indicated by a reference numeral 55, and the other normal-image-quality region is indicated by a reference numeral 56. Note that, although FIG. 3(A) and FIG. 3(B) are different from the actual endoscopic image 51, for convenience of illustration, the low-image-quality region 55 is shown as a blank portion, and the normal-image-quality region 56 is shown as a shaded portion. The low-image-quality region 55 having a luminance equal to or greater than a certain threshold often occurs in the endoscopic image 51 when the image is captured in a state where halation, blur, blur, water sprinkling occurs, or when the image is in close contact with a mucous membrane. Therefore, the luminance analysis information of the low-image-quality region 55 is the criterion for selecting the learning endoscopic image 52. In this embodiment, a preset value is used as the certain threshold used for outputting the luminance analysis information.

[0041] As shown in FIG. 3(C), the brightness analysis unit 16 outputs size information of the low image quality region 55 having a brightness equal to or greater than a certain threshold value as brightness analysis information for each of the target endoscopic image 51S and the surrounding endoscopic images 51A and 51B. Specifically, the brightness analysis unit 16 calculates a first pixel number 61, which is the number of pixels of the low image quality region 55 having a brightness equal to or greater than a certain threshold value, for the target endoscopic image 51S, and calculates second pixel numbers 62A and 62B, which are the number of pixels of the low image quality region 55 having a brightness equal to or greater than a certain threshold value, for the surrounding endoscopic images 51A and 51B. The first pixel number 61 is a count of the number of pixels included in the low image quality region 55 in the target endoscopic image 51S, and the second pixel numbers 62A and 62B are a count of the number of pixels included in the low image quality region 55 in the surrounding endoscopic images 51A and 51B, respectively. The low image quality region 55 may be an area identified by image recognition processing. Images containing low-image-quality areas such as halation, blur, shaking, and water spatter may be trained in advance, and the low-image-quality areas may be identified using the trained model.

[0042] The luminance analysis unit 16 outputs the first pixel number 61 and the second pixel numbers 62A and 62B as luminance analysis information. The first pixel number 61 and the second pixel numbers 62A and 62B output from the luminance analysis unit 16 are input to the image selection unit 17 together with the target endoscopic image 51S.

[0043] The image selection unit 17 compares the first pixel number 61 with the second pixel numbers 62A, 62B to determine whether or not to select the target endoscopic image 51S as the learning endoscopic image 52. In this embodiment, if the first pixel number 61 is greater than the maximum value of the second pixel numbers 62A, 62B (i.e., the largest value of the second pixel numbers 62A, 62B), the target endoscopic image 51S is excluded from the learning endoscopic images 52.

[0044] A series of steps in which the medical image processing device 11 acquires an endoscopic video 50 and selects a learning endoscopic image 52 to be used in machine learning will be described with reference to the flowchart shown in FIG. 4. The image acquisition unit 15 sequentially acquires the endoscopic video 50 from the endoscopic processor device 103 (S101). The brightness analysis unit 16 first determines a target endoscopic image 51S and surrounding endoscopic images 51A and 51B from among the multiple endoscopic images constituting the endoscopic video 50 (S102). The brightness analysis unit 16 calculates a first pixel number 61 for the target endoscopic image 51S, calculates second pixel numbers 62A and 62B for the surrounding endoscopic images 51A and 51B, and outputs them as brightness analysis information (S103).

[0045] The image selection unit 17 compares the first pixel number 61 with the second pixel numbers 62A, 62B (S104). If the first pixel number 61 is greater than the maximum value of the second pixel numbers 62A, 62B (Y in S105), the target endoscopic image 51S is not selected as the learning endoscopic image 52, and if the first pixel number 61 is equal to or less than the maximum value of the second pixel numbers 62A, 62B (N in S105), the target endoscopic image 51S is selected as the learning endoscopic image 52 (S106).

[0046] The storage control unit 18 stores the target endoscopic image 51S selected by the image selection unit 17 in the storage device 13 as the learning endoscopic image 52 (S107). If the first pixel count 61 is greater than the maximum value of the second pixel counts 62A, 62B (Y in S105) and the target endoscopic image 51S is not selected as the learning endoscopic image 52, the storage control unit 18 does not store the target endoscopic image 51S in the storage device 13.

[0047] Thereafter, the selection process continues (Y in S108) and the same process is repeated for the endoscopic images 51 included in the specific time range TR (S101 to S107). When the selection process has been performed for all the endoscopic images 51 included in the specific time range TR (N in S108), the medical image processing device 11 terminates all the processes.

[0048] As described above, in the medical image processing device 11, the luminance analysis information is output for a specific endoscopic image among the multiple endoscopic images 51 constituting the endoscopic video 50, and the luminance analysis information is used to select the learning endoscopic image 52, so that the endoscopic image 51 with low image quality can be automatically excluded. In other words, the endoscopic image suitable for machine learning can be efficiently selected. Therefore, it is no longer necessary for the user, the doctor, to visually check and exclude the endoscopic images with low image quality, and the doctor's effort and work time can be saved.

[0049] In addition, the medical image processing device 11 calculates a first pixel number 61, which is the number of pixels in the low image quality region 55, for the target endoscopic image 51S, and calculates second pixel numbers 62A, 62B, which are the number of pixels in the low image quality region 55, for the surrounding endoscopic images 51A, 51B, and selects the learning endoscopic image 52 by comparing the first pixel number 61 with the second pixel numbers 62A, 62B, so that endoscopic images suitable for machine learning can be selected more accurately and efficiently.

[0050] In the above first embodiment, one frame image each captured immediately before and after the target endoscopic image 51S is set as the surrounding endoscopic images 51A, 51B, but the number of surrounding endoscopic images is not limited to this, and a total of three or more frame images captured before and after the target endoscopic image 51S may be set as the surrounding endoscopic images. When multiple surrounding endoscopic images are used, the size information of multiple low image quality regions 55 may be averaged and compared with the target endoscopic image.

[0051] In the above first embodiment, the first pixel number 61 is calculated for the target endoscopic image 51S, and the second pixel numbers 62A, 62B are calculated for the surrounding endoscopic images 51A, 51B, and if the first pixel number 61 is greater than the maximum value of the second pixel numbers 62A, 62B, the target endoscopic image 51S is excluded from the training endoscopic image 52. However, the present invention is not limited to this, and the target endoscopic image 51S may be excluded from the training endoscopic image 52 if the first pixel number 61 is greater than the median or average value of the second pixel numbers 62A, 62B.

[0052] [Second embodiment] In the first embodiment, among the multiple endoscopic images constituting the endoscopic video, the first pixel number is calculated for the target endoscopic image and the second pixel number is calculated for the surrounding endoscopic images. However, the present invention is not limited to this. For each of the multiple specific endoscopic images, size information of a low-image-quality area having a luminance equal to or higher than a certain threshold is output as luminance analysis information, and the size information of the specific endoscopic images is compared to select the learning endoscopic image 52. In this embodiment, the dimensions of the low-image-quality area are compared as size information to select the learning endoscopic image 52. Note that the medical image processing device to which this embodiment is applied is similar to the medical image processing device 11 of the first embodiment, except for the luminance analysis and image selection process, and the same reference numerals are used to designate the same configurations and functions, and description thereof will be omitted.

[0053] As shown in Figure 5 (A), the brightness analysis unit 16 analyzes the brightness information of a target endoscopic image 51S (target medical image) and surrounding endoscopic images 51A and 51B (surrounding medical images) different from the target endoscopic image 51S as specific endoscopic images that fall within a specific time range TR among the multiple endoscopic images that make up the endoscopic video 50.

[0054] Note that, similarly to the first embodiment, the image acquisition unit 15 sequentially acquires the endoscopic videos 50 transmitted from the endoscope processor device 103 of the endoscope system 100. Also, in the example shown in Fig. 5(A), similarly to the first embodiment, the brightness analysis unit 16 sets the endoscopic images 51, which are frame images captured immediately before and immediately after the target endoscopic image 51S, among the multiple endoscopic images 51 arranged in chronological order, as surrounding endoscopic images 51A and 51B.

[0055] As shown in Fig. 5(B), the brightness analysis unit 16 outputs the dimensions of the low image quality region 55 having a brightness equal to or greater than a certain threshold value as size information 71, 72A, 72B for each of the target endoscopic image 51S and the surrounding endoscopic images 51A and 51B. Specifically, the brightness analysis unit 16 outputs the maximum dimension LMAX (see Fig. 6) of the low image quality region 55 having a brightness equal to or greater than a certain threshold value as size information 71, 72A, 72B for each of the target endoscopic image 51S and the surrounding endoscopic images 51A and 51B. Note that the size information output by the brightness analysis unit 16 is not limited to this, and may be any information related to the dimensions of the low image quality region 55 having a brightness equal to or greater than a certain threshold value. For example, the dimension LX (see Fig. 6) in the left-right direction X of the endoscopic image 51 and the dimension LY (see Fig. 6) in the up-down direction Y may be used as size information.

[0056] The image selection unit 17 determines whether or not to select the target endoscopic image 51S as the learning endoscopic image 52 by comparing the size information 71, 72A, 72B of the target endoscopic image 51S and the surrounding endoscopic images 51A, 51B.

[0057] 5(C), the image selection unit 17 calculates a change amount 73A between the size information 72A and the size information 71, and a change amount 73B between the size information 71 and the size information 72B. The image selection unit 17 uses the changes 73A and 73B to determine whether or not to select the target endoscopic image 51S as the learning endoscopic image 52. For example, if either of the changes 73A and 73B is greater than a certain threshold, the target endoscopic image 51S is excluded from the learning endoscopic images 52.

[0058] A series of steps in which the medical image processing device in this embodiment selects a training endoscopic image 52 to be used for machine learning will be described with reference to the flowchart shown in Fig. 7. The process of acquiring the endoscopic video 50 (S201) and determining the target endoscopic image 51S and the surrounding endoscopic images 51A and 51B (S202) is the same as S101 and S102 in the flowchart in the first embodiment. The brightness analysis unit 16 outputs size information 71, 72A, and 72B, which are dimensions of low-image-quality regions 55 having brightness above a certain threshold, for the target endoscopic image 51S and the surrounding endoscopic images 51A and 51B, as brightness analysis information (S203).

[0059] As described above, the image selection unit 17 calculates the change amount 73A between the size information 72A and the size information 71, and the change amount 73B between the size information 71 and the size information 72B (S204). If either of the change amounts 73A and 73B is greater than a certain threshold (Y in S205), the image selection unit 17 does not select the target endoscopic image 51S as the learning endoscopic image 52. On the other hand, if both of the change amounts 73A and 73B are less than the certain threshold (N in S205), the image selection unit 17 selects the target endoscopic image 51S as the learning endoscopic image 52 (S206). The storage control unit 18 stores the target endoscopic image 51S selected by the image selection unit 17 in the storage device 13 as the learning endoscopic image 52 (S207). Note that if the target endoscopic image 51S is not selected as the learning endoscopic image 52 (Y in S205), the storage control unit 18 does not store the target endoscopic image 51S in the storage device 13.

[0060] Thereafter, the selection process continues (Y in S208) and the same process is repeated for the endoscopic images 51 included in the specific time range TR (S201 to S207). When the selection process has been performed for all the endoscopic images 51 included in the specific time range TR (N in S208), the medical image processing device 11 terminates all the processes.

[0061] As described above, in the medical image processing device of this embodiment, among the multiple endoscopic images 51 constituting the endoscopic video 50, size information is output as brightness analysis information for the target endoscopic image 51S and the surrounding endoscopic images 51A and 51B, and the size information is used to select the learning endoscopic image 52. Therefore, as in the first embodiment, it is possible to efficiently select endoscopic images suitable for machine learning, and it is possible to save the doctor's effort and work time.

[0062] In the above second embodiment, the image selection unit 17 uses the amount of change 73A between the size information 72A and the size information 71, and the amount of change 73B between the size information 71 and the size information 72B to determine whether or not to select the target endoscopic image 51S as the learning endoscopic image 52, but the present invention is not limited to this, and the image selection unit 17 may use the difference 74 (see FIG. 5(D)) between the amount of change 73A and the amount of change 73B to determine whether or not to select the target endoscopic image 51S as the learning endoscopic image 52. In this case, if the difference 74 is greater than a certain threshold, the target endoscopic image 51S is excluded from the learning endoscopic images 52.

[0063] [First Modification] In the second embodiment, the image selection unit 17 selects the learning endoscopic image 52 using the size information of the target endoscopic image 51S and the surrounding endoscopic images 51A and 51B, but the present invention is not limited to this, and size information and position information of a low-image-quality region 55 having a luminance equal to or higher than a certain threshold value for each of a plurality of specific endoscopic images may be output as luminance analysis information, and the image selection unit 17 may compare the size information and position information of the plurality of specific endoscopic images to select the learning endoscopic image 52. That is, in addition to comparing the size information of the low-image-quality region 55 described in the first and second embodiments, a comparison of the position information of the low-image-quality region 55 is performed as shown in FIG.

[0064] 8, low image quality regions 55A, 55B of target endoscopic image 51S are indicated by solid lines, and low image quality regions 55 of surrounding endoscopic images 51A, 51B are indicated by two-dot chain lines. As position information P1 to P3 of low image quality region 55, for example, the coordinates of the centroid of low image quality region 55 are calculated.

[0065] For example, when there are a plurality of low-image-quality regions 55 in the target endoscopic image 51S (two in the example shown in FIG. 8), the distances D1 and D2 between the position information P1 and P2 of the low-image-quality regions 55A and 55B in the target endoscopic image 51S and the position information P3 of the low-image-quality region 55C in the surrounding endoscopic images 51A and 51B are calculated. Then, the image selection unit 17 selects the low-image-quality region 55A that is close to the position information P3 of the low-image-quality region 55 in the surrounding endoscopic images 51A and 51B, that is, the low-image-quality region 55A with the short distances D1 and D2, as the object of comparison. Specifically, the image selection unit 17 associates the low-image-quality region 55 in the surrounding endoscopic images 51A and 51B with the low-image-quality region 55A in the target endoscopic image 51S. Next, the image selection unit 17 selects the learning endoscopic image 52 using the size information of the low-image-quality region 55A and the size information of the low-image-quality region 55 in the surrounding endoscopic images 51A and 51B, as in the above-mentioned embodiments.

[0066] [Third embodiment] In the first and second embodiments, size information is output for each of the target endoscopic image and the surrounding endoscopic image, and the size information of the target endoscopic image and the surrounding endoscopic image are compared to select the learning endoscopic image 52, but in this embodiment, the target endoscopic image is divided into a plurality of regions, divided region luminance analysis information is output for each divided region of the target endoscopic image, surrounding luminance analysis information is calculated for the surrounding endoscopic image, and the divided region luminance analysis information and surrounding luminance analysis information are compared to select the learning endoscopic image 52. Note that the medical image processing device to which this embodiment is applied is similar to the medical image processing device 11 of the first embodiment, except for the luminance analysis and image selection processes, and similar configurations and functions are denoted by the same reference numerals and description thereof is omitted.

[0067] As shown in Figure 9 (A), the brightness analysis unit 16 analyzes the brightness information of a target endoscopic image 51S (target medical image) and surrounding endoscopic images 51A and 51B (surrounding medical images) different from the target endoscopic image 51S as specific endoscopic images that fall within a specific time range TR among the multiple endoscopic images that constitute the endoscopic video 50.

[0068] Note that, similarly to the first embodiment, the image acquisition unit 15 sequentially acquires the endoscopic videos 50 transmitted from the endoscope processor device 103 of the endoscope system 100. Also, in the example shown in Fig. 9(A), similarly to the first embodiment, the brightness analysis unit 16 sets the endoscopic images 51, which are frame images captured immediately before and immediately after the target endoscopic image 51S, among the multiple endoscopic images 51 arranged in chronological order, as surrounding endoscopic images 51A and 51B.

[0069] As shown in FIG. 9(B), the brightness analysis unit 16 divides the target endoscopic image 51S into a plurality of grids 80 (a plurality of regions). The grids 80 refer to the respective square regions obtained by dividing the target endoscopic image 51S into a grid. In the example shown in FIG. 9(B), the target endoscopic image 51S is divided into 4×4 grids, and a total of 16 grids 80 are set. Note that the size and number of divisions of the grids 80 are not limited to this, and may be appropriately changed depending on the number of pixels of the endoscopic image 51 and the processing capacity of the brightness analysis unit 16. Note that FIG. 9(C) illustrates one of the grids 80 divided into the target endoscopic image 51S, enlarged from FIG. 9(B).

[0070] 9(D), the brightness analysis unit 16 calculates grid brightness analysis information (divided region brightness analysis section) of the low image quality region 55 having a brightness equal to or higher than a certain threshold value for each grid 80 of the target endoscopic image 51S. In this embodiment, the brightness analysis unit 16 calculates a third pixel number 81, which is the number of pixels in the low image quality region 55, as the grid brightness analysis information for each grid 80. Note that the third pixel number 81 is obtained by counting the number of pixels included in the low image quality region 55 for each grid 80.

[0071] 9(D), the luminance analysis unit 16 calculates second pixel numbers 82A, 82B, which are the numbers of pixels in the low image quality region 55 having a luminance equal to or higher than a certain threshold, as surrounding luminance analysis information for the surrounding endoscopic images 51A, 51B. Note that the second pixel numbers 82A, 82B calculated by the luminance analysis unit 16 are similar to the second pixel numbers 62A, 62B calculated by the luminance analysis unit 16 in the first embodiment.

[0072] The luminance analysis unit 16 outputs the third pixel number 81 and the second pixel numbers 82A and 82B as luminance analysis information. The third pixel number 81 and the second pixel numbers 82A and 82B output from the luminance analysis unit 16 are input to the image selection unit 17 together with the target endoscopic image 51S.

[0073] The image selection unit 17 extracts the low image quality region 55 in the grid 80 as a target region 83 (see FIG. 10B) by comparing the second pixel counts 82A, 82B in the surrounding endoscopic images 51A, 51B with the third pixel count 81 in at least one grid 80. Specifically, if the third pixel count 81 in the grid 80 is greater than the maximum value of the second pixel counts 82A, 82B in the surrounding endoscopic images 51A, 51B, the low image quality region 55 in the grid 80 corresponding to the third pixel count 81 is set as the target region 83. Then, if the total pixel count 84 (see FIG. 10C) of the target region 83 is equal to or greater than a certain percentage of the pixel count 85 (see FIG. 10C) of the target endoscopic image 51S, the image selection unit 17 excludes the target endoscopic image 51S from the learning endoscopic images 52. However, the present invention is not limited to this, and the image selection unit 17 may exclude the target endoscopic image 51S from the training endoscopic image 52 if the maximum, average, or median number of pixels in the target area 83 is greater than or equal to a certain percentage of the number of pixels in the target endoscopic image 51S.

[0074] In the example shown in Fig. 10, among a plurality of grids 80 (see Fig. 10(A)) obtained by dividing the target endoscopic image 51S, the third pixel count 81 in two grids 80A, 80B is greater than the maximum value of the second pixel counts 82A, 82B in the surrounding endoscopic images 51A, 51B. Therefore, the image selection unit 17 sets the low image quality region 55 in the grids 80A, 80B corresponding to the third pixel count 81 as the target region 83 (see Fig. 10(B)). Note that Fig. 10(B) illustrates the grids 80A, 80B including the target region 83 enlarged with respect to Fig. 10(A).

[0075] The image selection unit 17 excludes the target endoscopic image 51S from the learning endoscopic image 52 if the total number of pixels 84 in the target area 83, i.e., the total number of pixels 84 in the target area 83 within the grids 80A and 80B, is greater than a certain percentage of the number of pixels 85 in the target endoscopic image 51S.

[0076] A series of steps in which the medical image processing device in this embodiment selects a training endoscopic image 52 to be used for machine learning will be described with reference to the flowchart shown in Fig. 11. The processes of acquiring the endoscopic video 50 (S301) and determining the target endoscopic image 51S and the surrounding endoscopic images 51A and 51B (S302) are similar to the processes of S101 and S102 in the flowchart in the first embodiment. The brightness analysis unit 16 divides the target endoscopic image 51S into a plurality of grids 80 (S303).

[0077] The luminance analysis unit 16 calculates second pixel numbers 82A and 82B, which are the number of pixels in the low image quality region 55 having a luminance equal to or greater than a certain threshold, for the surrounding endoscopic images 51A and 51B, respectively (S304). Meanwhile, the luminance analysis unit 16 calculates a third pixel number 81, which is the number of pixels in the low image quality region 55 having a luminance equal to or greater than a certain threshold, for each grid 80 of the target endoscopic image 51S (S305). The second pixel numbers 82A and 82B and the third pixel number 81 are output as luminance analysis information (S306).

[0078] The image selection unit 17 compares the second pixel counts 82A, 82B in the peripheral endoscopic images 51A, 51B with the third pixel count 81 in at least one grid 80 (S307), and if the third pixel count 81 is greater than the maximum value of the second pixel counts 82A, 82B (Y in S308), it determines the low-image-quality region 55 in the grid 80 corresponding to the third pixel count 81 as the target region 83 (S309). Note that if the third pixel count 81 is equal to or less than the maximum value of the second pixel counts 82A, 82B (N in S308), it does not determine the low-image-quality region 55 in the grid 80 corresponding to the third pixel count 81 as the target region 83, and selects the target endoscopic image 51S as the learning endoscopic image 52 (S311).

[0079] Then, if the total number of pixels in the target area 83 is greater than or equal to a certain percentage of the number of pixels in the target endoscopic image 51S (Y ​​in S310), the image selection unit 17 does not select the target endoscopic image 51S as the learning endoscopic image, and if the total number of pixels in the target area 83 is less than a certain percentage of the number of pixels in the target endoscopic image 51S (N in S310), the image selection unit 17 selects the target endoscopic image 51S as the learning endoscopic image 52 (S311).

[0080] The storage control unit 18 stores the target endoscopic image 51S selected by the image selection unit 17 in the storage device 13 as a learning endoscopic image (S312). If the total number of pixels in the target region 83 is less than a certain ratio of the number of pixels in the target endoscopic image 51S (N in S310) and the target endoscopic image 51S is not selected as a learning endoscopic image, the storage control unit 18 does not store the target endoscopic image 51S in the storage device 13.

[0081] Thereafter, the selection process continues (Y in S313) and the same process is repeated for the endoscopic images 51 included in the specific time range TR (S301 to S312). When the selection process has been performed for all the endoscopic images 51 included in the specific time range TR (N in S313), the medical image processing device 11 terminates all the processes.

[0082] As described above, in the medical image processing device of this embodiment, the target endoscopic image 51S is divided into a plurality of grids 80, grid luminance analysis information is output for each grid 80, surrounding luminance analysis information is calculated for the surrounding endoscopic images 51A and 51B, and the grid luminance analysis information is compared with the surrounding luminance analysis information to select the learning endoscopic image 52. Thus, as in the first embodiment, it is possible to efficiently select an endoscopic image suitable for machine learning, thereby saving the doctor's effort and work time.

[0083] In the above third embodiment, when determining whether or not to select the target endoscopic image 51S as the training endoscopic image 52, the target endoscopic image 51S is divided into a plurality of grids 80, a third pixel number 81 is output for each grid 80, and second pixel numbers 82A, 82B are calculated for the surrounding endoscopic images 51A, 51B. If the third pixel number 81 is greater than the maximum value of the second pixel numbers 82A, 82B, the target endoscopic image 51S is excluded from the training endoscopic image 52. However, the present invention is not limited to this, and the target endoscopic image 51S may be excluded from the training endoscopic image 52 if the third pixel number is greater than the median or average of the second pixel numbers.

[0084] In the third embodiment, the brightness analysis unit 16 calculates the third pixel number 81, which is the number of pixels of the low image quality region 55, for each grid 80 of the target endoscopic image 51S, and calculates the second pixel numbers 82A and 82B, which are the number of pixels of the low image quality region 55, for the surrounding endoscopic images 51A and 51B, respectively, and the image selection unit 17 selects the learning endoscopic image 52 using the second pixel numbers 82A and 82B and the third pixel number 81. However, the size information used to select the learning endoscopic image 52 is not limited to the second pixel numbers 82A and 82B and the third pixel number 81, and the dimensions of the low image quality region 55 or the like may be used as the size information, similar to the brightness analysis information in the second embodiment. In this case, the brightness analysis unit 16 may output divided region analysis information, which is size information of the low image quality region 55, for each grid 80 of the target endoscopic image 51S as brightness analysis information, calculate surrounding brightness analysis information, which is size information of the low image quality region 55, for the surrounding endoscopic images 51A and 51B, and the image selection unit 17 may compare the grid brightness analysis information with the surrounding brightness analysis information to select the learning endoscopic image 52 to be used for machine learning. In this case, for example, it is preferable to compare the grid brightness analysis information (size information) with the surrounding brightness analysis information (size information), and exclude the target endoscopic image 51S from the learning endoscopic images 52 if the grid brightness analysis information is greater than the maximum value, average value, or median value of the surrounding brightness analysis information.

[0085] In the third embodiment, the image selection unit 17 selects the learning endoscopic image 52 using size information for the grid 80 of the target endoscopic image 51S and the surrounding endoscopic images 51A and 51B, but the present invention is not limited to this. For the grid 80 of the target endoscopic image 51S and the surrounding endoscopic images 51A and 51B, size information and position information of the low-image-quality region 55 having a luminance equal to or higher than a certain threshold may be output as luminance analysis information, and the image selection unit 17 may compare the size information and position information of the low-image-quality region 55 for the grid 80 of the target endoscopic image 51S and the surrounding endoscopic images 51A and 51B to select the learning endoscopic image 52 to be used for machine learning from a plurality of specific medical images. That is, in addition to the comparison of size information described in the first to third embodiments, it is preferable to calculate position information in the same manner as in the first modified example and compare the position information of the low-image-quality region 55.

[0086] [Second modified example] In each of the above embodiments, a medical image processing device having the functions of the image acquisition unit 15, the brightness analysis unit 16, the image selection unit 17, the storage control unit 18, and the display control unit 19 has been described, but the present invention is not limited thereto, and as shown in FIG. 12, in addition to the above configuration, a learning unit 20 that performs machine learning may be provided. In this case, it is preferable that the learning unit 20 performs machine learning on the learning endoscopic image 52 selected as in each of the above embodiments, for example, to automatically detect a region of interest such as a lesion, and generate a CAD that highlights the detected region of interest. In addition, machine learning may be performed using the learning endoscopic image 52 selected by the image selection unit 17, and the image may be stored in the storage device 13.

[0087] In each of the above embodiments, the storage control unit 18 stores the learning endoscopic image 52 selected by the image selection unit 17 in the storage device 13 and does not store the unselected target endoscopic image 51S, but is not limited to this. The learning endoscopic image 52 may be saved in a first storage device and the unselected target endoscopic image 51S may be stored in a second storage device different from the first storage device. In this case, the learning medical image stored in the first storage device may be automatically transmitted to a server or the like provided outside the medical image processing device.

[0088] In each of the above embodiments, the brightness analysis unit 16 uses a preset value as the certain threshold value used when outputting the brightness analysis information, but the present invention is not limited thereto. Operation information, imaging conditions, or imaging device information when the endoscope system 100 captures an endoscopic image may be recorded, and the certain threshold value may be determined according to this information. Specifically, when information that is likely to cause a change in brightness is recorded, such as when the endoscope 102 is bent (when the tilt of the tip portion changes), when a zoom operation is performed, or when the amount of illumination light changes, it is preferable to change the certain threshold value. In addition, it is preferable to record the above-mentioned operation information, imaging conditions, imaging device information, etc., in association with the endoscopic video.

[0089] Also, similarly to the first modified example, position information of low image quality regions 55 having a luminance equal to or greater than a certain threshold may be calculated, and the certain threshold may be determined according to this position information. For example, since the lens of an endoscope is like a fisheye lens, the size of low image quality regions 55 is larger at the edge of the screen than at the center of the screen, so the certain threshold is increased for low image quality regions 55 determined to be at the edge of the screen from the position information. Alternatively, assuming that an area of ​​mucosa oblique to the light source and an area facing the light source are mixed in one endoscopic image 51, the size of low image quality regions 55 is larger in the oblique areas, so the certain threshold is increased for low image quality regions 55 determined to be areas where light is obliquely irradiated from the position information.

[0090] In 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. In addition, the medical images are not limited to endoscopic images, and medical videos can be acquired and learning medical images can be selected for medical images such as X-ray images, CT (Computed Tomography) images, and MR (Magnetic Resonance) images, in the same manner as in each of the above embodiments.

[0091] In each of the above embodiments, size information of areas having a brightness above a certain threshold, i.e., areas with high brightness (bright) is compared, and areas with large size information are excluded from the training medical image, but the present invention is not limited to this, and size information of areas having a brightness below a certain threshold, i.e., areas with low brightness (dark) is compared, and areas with large size information are excluded from the training medical image.

[0092] In the above-mentioned embodiments and modifications, the hardware structure of the processing units that execute various processes, such as the image acquisition unit 15, the brightness analysis unit 16, the image selection unit 17, the storage control unit 18, and the display control unit 19, is various processors as shown below. The various processors include a CPU (Central Processing Unit), which is a general-purpose processor that executes software (programs) and functions as various processing units, a programmable logic device (PLD), which is a processor whose circuit configuration can be changed after manufacture, such as an FPGA (Field Programmable Gate Array), a dedicated electric circuit, which is a processor having a circuit configuration designed specifically for executing various processes, and a GPU (Graphical Processing Unit) that performs a large amount of processing, such as image processing, in parallel.

[0093] One processing unit may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same type or different types (for example, multiple FPGAs, a combination of a CPU and an FPGA, or a combination of a CPU and a GPU). Also, multiple processing units may be configured with one processor. As an example of configuring multiple processing units with one processor, first, as represented by a computer such as a client or a server, there is a form in which one processor is configured with a combination of one or more CPUs and software, and this processor functions as multiple processing units. Second, as represented by a system on chip (SoC), there is a form in which a processor is used that realizes the functions of the entire system including multiple processing units with one IC (Integrated Circuit) chip. In this way, the various processing units are configured using one or more of the above various processors as a hardware structure.

[0094] Furthermore, the hardware structure of these various processors is, more specifically, an electric circuit in the form of a combination of circuit elements such as semiconductor elements. [Explanation of symbols]

[0095] 10 Medical image processing system 11 Medical image processing equipment 12 Display 13 Storage device 15 Image acquisition section 16 Luminance Analysis Section 17 Image selection section 18 Memory control unit 19 Display control section 20 Learning Department 50 Endoscopy Videos 51 Endoscopic Images 51A, 51B Peripheral endoscope images 51S Target endoscope image 52 Endoscopic images for learning 55, 55A, 55B Low image quality area 56 Normal image quality area 61 1st pixel count 62A, 62B Second pixel count 71, 72A, 72B Size Information 73A, 73B Change 74 Difference 80 Grid 80A, 80B Grid 81 3rd pixel count 82A, 82B 2nd pixel count 83 Target Area 84 Total pixels in the target area 85 Number of pixels in the target endoscopic image 100 Endoscope system 101 Light source device 102 Endoscope 103 Endoscope processor device 104 Display P1, P2, P3 location information LMAX Maximum dimension LX Dimension in the left-right direction LY Dimension in the vertical direction TR Specific time range X Left / right direction Y vertical direction

Claims

1. A processor is provided. The processor, Get medical videos, Analyzing luminance information of a plurality of specific medical images within a specific time range among the plurality of medical images constituting the medical video, and outputting luminance analysis information; A medical image processing device that uses the brightness analysis information to select a learning medical image to be used for machine learning from the plurality of specific medical images.

2. The processor, outputting size information of an area having a luminance equal to or greater than a certain threshold value as luminance analysis information for each of the plurality of specific medical images; The medical image processing apparatus according to claim 1 , further comprising: a processor for processing the image data of the plurality of specific medical images;

3. The processor, outputting size information and position information of a region having a luminance equal to or greater than a certain threshold value as luminance analysis information for each of the plurality of specific medical images; The medical image processing apparatus according to claim 1 , further comprising: a processor for processing the medical images based on the plurality of specific medical images;

4. The processor, Calculating the size information for a target medical image and a peripheral medical image different from the target medical image among a plurality of medical images constituting the medical video; Calculating a change amount of the size information between the target medical image and the surrounding medical images; The medical image processing apparatus according to claim 2 or 3, further comprising: a step of determining whether or not to select the target medical image as the learning medical image using the amount of change.

5. The processor, Calculating the size information for a target medical image and a plurality of peripheral medical images different from the target medical image among the plurality of medical images constituting the medical video; calculating a difference in an amount of change between the target medical image and the plurality of peripheral medical images using the size information; The medical image processing apparatus according to claim 2 , further comprising: a step of determining whether or not to select the target medical image as the learning medical image by using the difference.

6. The processor, Calculating a first pixel number, which is the number of pixels in an area having a luminance equal to or greater than a certain threshold, for a target medical image among the plurality of specific medical images; Calculating a second pixel number, which is the number of pixels of an area having a luminance equal to or greater than a certain threshold, for a peripheral medical image different from the target medical image among the plurality of specific medical images; outputting the first number of pixels and the second number of pixels as the luminance analysis information; The medical image processing apparatus according to claim 1 , further comprising: a processor for determining whether or not to select the target medical image as the learning medical image by comparing the first pixel count with the second pixel count.

7. The processor, The medical image processing apparatus according to claim 6 , wherein if the first pixel count is greater than a maximum value, an average value, or a median value of the second pixel count, the target medical image is excluded from the learning medical images.

8. The processor, Dividing a target medical image among the plurality of specific medical images into a plurality of regions; outputting, as the brightness analysis information, divided region brightness analysis information which is size information of a region having a brightness equal to or greater than a certain threshold value for each of the plurality of regions of the target medical image; For a surrounding medical image different from the target medical image, surrounding brightness analysis information is calculated, which is size information of an area having a brightness equal to or greater than a certain threshold value; The medical image processing apparatus according to claim 1 , further comprising: a processor for selecting a learning medical image to be used for machine learning from the plurality of specific medical images by comparing the divided region luminance analysis information with the surrounding luminance analysis information.

9. The processor, Dividing a target medical image among the plurality of specific medical images into a plurality of regions; outputting, as the brightness analysis information, divided region brightness analysis information which is size information and position information of a region having a brightness equal to or greater than a certain threshold value for each of the plurality of regions of the target medical image; For a surrounding medical image different from the target medical image, calculate surrounding brightness analysis information, which is size information and position information of an area having a brightness equal to or higher than a certain threshold value; The medical image processing apparatus according to claim 1 , further comprising: a processor for selecting a learning medical image to be used for machine learning from the plurality of specific medical images by comparing the divided region luminance analysis information with the surrounding luminance analysis information.

10. The processor, Dividing a target medical image among the plurality of specific medical images into a plurality of regions; Calculating a second pixel number, which is the number of pixels of an area having a luminance equal to or greater than a certain threshold, for a peripheral medical image different from the target medical image among the plurality of specific medical images; Calculating a third pixel number, which is the number of pixels in an area having a luminance equal to or greater than a certain threshold value, for each of the plurality of areas; outputting the second number of pixels and the third number of pixels as the luminance analysis information; extracting, as a target region, a region having a luminance equal to or greater than a certain threshold value from among the plurality of regions by comparing the second pixel count with the third pixel count in at least one of the plurality of regions; The medical image processing apparatus according to claim 1 , wherein the target medical image is compared with the target region to determine whether or not to select the target medical image as the learning medical image.

11. The processor, A medical image processing device as described in claim 10, wherein if the third pixel number is greater than the maximum value, average value, or median value of the second pixel number, a region within the multiple regions corresponding to the third pixel number and having a brightness equal to or greater than the certain threshold value is determined to be the target region.

12. The processor, The medical image processing apparatus according to claim 10 or 11, wherein if the total number of pixels in the target region is equal to or greater than a certain ratio of the number of pixels in the target medical image, the target medical image is excluded from the learning medical images.

13. The processor, A medical image processing device as described in any one of claims 10 to 12, wherein if the maximum, average, or median number of pixels in the target region is greater than or equal to a certain percentage of the number of pixels in the target medical image, the target medical image is excluded from the learning medical images.

14. The processor, The medical image processing device according to claim 2 , wherein the constant threshold value used when outputting the luminance analysis information is determined according to operation information, imaging conditions, or an imaging device when the medical image is captured.

15. The processor, The medical image processing apparatus according to claim 1 , further comprising: analyzing the luminance analysis information from three or more of the specific medical images to select the learning medical image.

16. The processor, The medical image processing apparatus according to claim 1 , wherein the learning medical images are stored in a storage device.

17. The medical image processing apparatus according to claim 1 , wherein the processor performs machine learning using the selected medical images for learning.

18. acquiring a medical video; a step of analyzing luminance information of a plurality of specific medical images within a specific time range among the plurality of medical images constituting the medical video, and outputting luminance analysis information; selecting a learning medical image to be used for machine learning from the plurality of specific medical images using the brightness analysis information; A method of operating a medical imaging device comprising:

Images