IMAGE PROCESSING DEVICE, IMAGE PROCESSING METHOD, AND RECORDING MEDIUM

The endoscopic image acquisition means acquires an endoscopic image. The polyp detection means detects a polyp area from the endoscopic image. The first estimation means estimates a size of the polyp based on an image of the detected polyp area. The output means outputs an estimation result of the size of the polyp.

TECHNICAL FIELD

The present disclosure relates to processing of images relating to an endoscopic examination.

BACKGROUND ART

When performing treatment of polyp in an endoscopic examination, a doctor selects a treatment instrument according to the polyp size. At this time, the doctor should select the treatment instrument after discerning the polyp in millimeters (mm). However, since the doctor measures the polyp size visually, the measurement in mm units was not enough and it was difficult to judge the treatment method. Patent Document 1 proposes a method of recognizing a target area with high accuracy from an image captured by an endoscopic imaging device.

PRECEDING TECHNICAL REFERENCES

Patent Document

Patent Document 1: International Publication WO2021/140602

SUMMARY

Problem to be Solved

However, according to Patent Document 1, it is not always possible to determine the polyp size with high accuracy.

It is an object of the present disclosure to provide an image processing device capable of estimating the size of polyp found in the endoscopic examination with high accuracy.

Means for Solving the Problem

According to an example aspect of the present disclosure, there is provided an image processing device comprising:an endoscopic image acquisition means configured to acquire an endoscopic image;a polyp detection means configured to detect a polyp area from the endoscopic image;a first estimation means configured to estimate a size of the polyp based on an image of the detected polyp area; andan output means configured to output an estimation result of the size of the polyp.

According to another example aspect of the present disclosure, there is provided an image processing method comprising:acquiring an endoscopic image;detecting a polyp area from the endoscopic image;estimating a size of the polyp based on an image of the detected polyp area; andoutputting an estimation result of the size of the polyp.

According to still another example aspect of the present disclosure, there is provided a recording medium storing a program, the program causing a computer to execute processing of:acquiring an endoscopic image;detecting a polyp area from the endoscopic image;estimating a size of the polyp based on an image of the detected polyp area; andoutputting an estimation result of the size of the polyp.

Effect

According to the present disclosure, it is possible to estimate the size of polyp found in the endoscopic examination with high accuracy.

EXAMPLE EMBODIMENTS

First Example Embodiment

FIG.1shows a schematic configuration of an endoscopic examination system100. At the time of examination (including treatment) using the endoscope, when the endoscopic examination system100detects a polyp, the endoscopic examination system100estimates whether or not the size of detected polyp is a predetermined size or more and displays the result. Thus, the doctor can judge the treatment method according to the polyp size.

As shown inFIG.1, the endoscopic examination system100mainly includes an image processing device1, a display device2, and an endoscope3connected to the image processing device1.

The image processing device1acquires an image (i.e., a movie, hereinafter also referred to as an “endoscopic video Ic”) captured by the endoscope3during the endoscopic examination from the endoscope3and displays display data for the check by the examiner of the endoscopic examination on the display device2. Specifically, the image processing device1acquires a moving image of organs captured by the endoscope3as an endoscopic video Ic during the endoscopic examination. The image processing device1extracts a still image (frame image) from the endoscopic video Ic, and detects the polyp and estimates whether or not the size of detected polyp is the predetermined size or more using AI. Then, the image processing device1generates a display image including an endoscopic image, an estimation result of the polyp size, or the like.

The display device2is a display or the like for displaying images on the basis of the display signal supplied from the image processing device1.

The endoscope3mainly includes an operation unit36used by the examiner to input instructions such as air supply, water supply, angle adjustment, and the photographing instruction, a shaft37having flexibility and inserted into an organ of a subject to be examined, a tip portion38with a built-in image-taking unit such as an ultra-compact imaging element, and a connection unit39for connection with the image processing device1.

While the following explanation is mainly given on the processing of endoscopic examination for a large intestine, the subjects of examination may be gastrointestinal (digestive organs) such as the stomach, esophagus, small intestine, and duodenum, as well as the large intestine.

FIG.2shows a hardware configuration of the image processing device1. The image processing device1mainly includes a processor11, a memory12, an interface13, an input unit14, a light source unit15, a sound output unit16, and a data base (hereinafter referred to as “DB”)17. These elements are connected with each other via a data bus19.

The processor11executes a predetermined processing by executing a program stored in the memory12. The processor11is a processor such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and a TPU (Tensor Processing Unit). The processor11may be configured by multiple processors. The processor11is an example of a computer.

The memory12is configured by various volatile memories used as a working memory and non-volatile memories for storing information needed for the processing of the image processing device1, such as a RAM (Random Access Memory) and a ROM (Read Only Memory). Incidentally, the memory12may include an external storage device such as a hard disk connected to or incorporated in the image processing device1, and may include a storage medium such as a removable flash memory or a disk medium. The memory12stores a program for the image processing device1to execute processing in the present example embodiment.

Also, the memory12temporarily stores a series of endoscopic videos Ic taken by the endoscope3in the endoscopic examination, based on the control of the processor11. Further, the memory12temporarily stores the still images acquired from the endoscopic video Ic during endoscopic examination. These images are stored in the memory12in association with, for example, subject identification information (e.g., the patient ID) and time stamp information, etc.

The interface13performs an interface operation between the image processing device1and the external devices. For example, the interface13supplies the display data Id generated by the processor11to the display device2. Also, the interface13supplies the illumination light generated by the light source unit15to the endoscope3. Also, the interface13supplies an electrical signal indicating the endoscopic video Ic supplied from the endoscope3to the processor11. The interface13may be a communication interface such as a network adapter for wired or wireless communication with an external device, or may be a hardware interface compliant with a USB (Universal Serial Bus), SATA (Serial Advanced Technology Attachment), etc.

The input unit14generates an input signal based on the operation of the examiner. The input unit14is, for example, a button, a touch panel, a remote controller, a voice input device, or the like. The light source unit15generates the light to be delivered to the tip portion38of the endoscope3. The light source unit15may also incorporate a pump or the like for delivering water or air to be supplied to the endoscope3. The sound output unit16outputs the sound based on the control of the processor11.

The DB17stores the endoscopic images acquired by past endoscopic examinations of the subject, and lesion information. The lesion information includes lesion image and related information. The lesion includes the polyp (elevated lesion). The DB17may include an external storage device, such as a hard disk connected to or incorporated in the image processing device1, and may include a storage medium, such as a removable flash memory. Instead of providing the DB17in the endoscopic examination system100, the DB17may be provided in an external server or the like to acquire associated information from the server through communication.

FIG.3is a block diagram showing a functional configuration of the image processing device1. The image processing device1functionally includes an image capturing unit21, an image area recognition unit22, a polyp detection unit23, and a first estimation unit24.

The image processing device1receives the endoscopic video Ic from the endoscope3. The endoscopic video Ic is inputted into the image capturing unit21. The image capturing unit21extracts the still image (frame image) for each frame from the endoscopic video Ic. Here, the extracted frame image includes an area for displaying information about the subject and an area for displaying the image of the endoscope camera. The image capturing unit21outputs the extracted frame image to the image area recognition unit22.

The image area recognition unit22recognizes the area for displaying the image of the endoscope camera from the frame image generated by the image capturing unit21and cuts out only the area. The image area recognition unit22outputs the cut-out image of the area for displaying the image of the endoscope camera (hereinafter, referred to as “endoscopic image”) to the polyp detection unit23and the first estimation unit24.

The polyp detection unit23detects the polyp from the endoscopic image generated by the image area recognition unit22, and estimates the position of the polyp. Specifically, the polyp detection unit23resizes the endoscopic image generated by the image area recognition unit22to the size capable of image analysis by AI. The polyp detection unit23detects the polyp included in the resized image by using an image recognition model prepared in advance. This image recognition model is a model which is learned in advance so as to estimate the position of the polyp included in the endoscopic image, and is also called “polyp detection model” hereafter. When the polyp detection unit23detects the polyp, the polyp detection unit23generates coordinate information of rectangle surrounding the polyp area and outputs the coordinate information to the first estimation unit24.

The first estimation unit24estimates whether or not the polyp detected by the polyp detection unit23is equal to or larger than the predetermined size. Specifically, first, the first estimation unit24cuts out only the polyp area from the endoscopic image generated by the image area recognition unit22using the coordinate information generated by the polyp detection unit23. Next, the first estimation unit24resizes the image of the cut-out polyp area to a size capable of image analysis by AI. Then, the first estimation unit24estimates whether or not the polyp is equal to or larger than the predetermined size by using an image recognition model prepared in advance. The image recognition model is a model which is learned in advance so as to estimate whether or not the size of polyp included in the image is equal to or larger than the predetermined size from the image of the polyp area, and is also called “first size estimation model” hereafter.

Next, the first estimation unit24estimates whether or not the polyp is equal to or larger than the predetermined size using the first size estimation model. Now, if the predetermined size is set to “Xmm”, a polyp of Xmm or larger is set to a large polyp, and a polyp smaller than Xmm is set to a small polyp. Here, the predetermined size “Xmm” is a value which is determined in advance based on guidelines for endoscopic examination and the like. The first estimation unit24estimates the size of the polyp included in the image of the polyp area, and calculates a score indicating the probability that the polyp is the large polyp (referred to as a “large polyp score”) and a score indicating the probability that the polyp is the small polyp (referred to as a “small polyp score”). For example, the first estimation unit24calculates each score so that the sum of the large polyp score and the small polyp score is “1”.

Then, the first estimation unit24compares the large polyp score and the small polyp score with a predetermined threshold TH, and adopts the score that is larger than the threshold TH as the estimation result. For example, assuming that the threshold TH is “0.5”, the first estimation unit24estimates that the polyp is the large polyp (Xmm or larger) when the large polyp score is larger than the threshold TH, and the first estimation unit24estimates that the polyp is the small polyp (smaller than Xmm) when the small polyp score is larger than the threshold TH. The first estimation unit24generates the display data Id based on the endoscopic images and the estimation result of the polyp size and outputs the display data Id to the display device2.

As described above, in the present example embodiment, when estimating the polyp size based on the endoscopic image, the first estimation unit24can estimate the polyp size with high accuracy by using the image narrowed down the polyp area. In the above-described example, the first estimation unit24estimates the polyp size after cutting out the polyp area from the endoscopic image. Instead, the first estimation unit24may draw the rectangle surrounding the polyp area on the endoscopic image using the coordinate information of the polyp area detected by the polyp detection unit23, and estimate the polyp size based on the image of the area surrounded by the rectangle.

The first estimation unit24may perform drawing that surrounds the polyp area on the endoscopic image with the rectangle or the like, and output the image data to the display device2. This allows the doctor to view the display image and easily grasp the position of the polyp.

In the above-described configuration, the image capturing unit21and the image area recognition unit22are example of an endoscopic image acquisition means, the polyp detection unit23is an example of a polyp detection means, and the first estimation unit24is an example of a first estimation means, an output means, and a display control means.

Next, image display processing for performing the above-described display will be described.FIG.4is a flowchart of image display processing by the image processing device1. This processing is realized by the processor11shown inFIG.2, which executes a pre-prepared program and operates as each element shown inFIG.3.

First, the image capturing unit21acquires the endoscopic video Ic through the input unit14, and acquires the frame image41from the endoscopic video Ic (step S11). As shown, the frame image41includes the area for displaying information about the subject and the area for displaying the image of the endoscope camera (endoscopic image). The image area recognition unit22acquires the endoscopic image42from the frame image41acquired by the image capturing unit21(step S12).

Next, the polyp detection unit23resizes the endoscopic image42to the suitable size for the polyp detection by the polyp detection model, and generates the resized image43(step S13). Next, the polyp detection unit23detects the polyp from the resized image43using the polyp detection model (step S14). When the polyp detection unit23detects the polyp, the polyp detection unit23generates the coordinate information of the rectangle43xindicating the polyp area of the resized image43. The coordinates of the rectangle43xindicating the polyp area can be expressed, for example, by the coordinates (x, y) of the upper left point of the rectangle, and the width “w” and height “h” of the rectangle when the point is set as the origin.

Next, the polyp detection unit23performs coordinate transformation of the rectangle43xindicating the polyp area (step S15). The coordinate transformation modifies the coordinates of the rectangle43xto the coordinates in the coordinate system prior to resizing in the step S13. This modifies the rectangle43xafter coordinate conversion to the size in the coordinate system of the endoscopic image42. The polyp detection unit23outputs the coordinate information of the polyp area after the coordinate transformation to the first estimation unit24.

Next, the first estimation unit24crops the polyp area from the endoscopic image42based on the coordinate information of the rectangle43xindicating the polyp area (step S16). The term crop refers to cutting out a part of an image.

Next, the first estimation unit24resizes the cropped polyp area44and generates the resized image45(step S17). The resizing here modifies the polyp area44to the suitable size for the first size estimation model used by the first estimation unit24. Next, the first estimation unit24estimates the polyp size based on the resized image45using the first size estimation model (step S18). Specifically, the first estimation unit24calculates the score indicating the probability that the polyp is equal to or larger than Xmm (large polyp score) and the score indicating the probability that the polyp is smaller than Xmm (small polyp score). Next, the first estimation unit24compares the calculated scores with the threshold TH, and displays the estimation result indicating whether or not the polyp is equal to or larger than Xmm (step S22). Then, the image display processing ends.

Next, a display example by the display device2will be described.FIG.5shows a display example of the estimation result of polyp size. At the time of endoscopic examination of a patient, when a polyp is detected, the endoscopic image, the detection result of the polyp, the estimation result of polyp size are displayed on the display device2.

In the display example ofFIG.5, the endoscope image61, the polyp detection image62, and the estimation result of polyp size63are displayed in the display area60.

The endoscope image61is the endoscopic image of the organ site where the polyp was detected. The polyp detection image62is the image displaying the polyp detected by the polyp detection unit23, and shows the polyp reflected in the endoscopic image by surrounding with rectangle62x.In this example, the polyp is surrounded by the rectangle, but it may be surrounded by other figures such as an ellipse. The estimation result of polyp size63is the estimation result of polyp size by the first estimation unit24. In this instance, the word “Non-diminutive” is displayed when the polyp size is equal to or larger than the predetermined size (Xmm), and the word “Diminutive” is displayed when the size of the polyp is smaller than the predetermined threshold. In addition to whether or not the polyp size is equal to or larger than the predetermined size, other information related to the polyp such as the grade of malignancy of the polyp may be displayed in the estimation result of polyp size63.

Next, a modification of the first example embodiment will be described.

The following modification can be applied to the first example embodiment.

In the first example embodiment, the first estimation unit24estimates the polyp size based on the image of the polyp area. Instead, the first estimation unit24may estimate the polyp size using optical flow data in addition to the image of the polyp area.FIG.6is a block diagram showing a functional configuration of an image display device1aof the modification 1. As shown, in the modification 1, an optical flow calculation unit28is provided on the image display device1a.The optical flow calculation unit28acquires the endoscopic image from the image area recognition unit22, and calculates the optical flow information based on one frame preceding endoscopic image and the latest endoscopic image. Then, the optical flow calculation unit28outputs the calculated optical flow information to the first estimation unit24. The optical flow calculation unit is an example of a calculation means.

The first size estimation model used by the first estimation unit24is the learned model which is learned in advance so as to estimate the polyp size based on the image of the polyp area and the optical flow information. The first estimation unit24estimates the polyp size based on the image of the polyp area inputted by the polyp detection unit23and the optical flow information calculated by the optical flow calculation unit28. Since the optical flow indicates the movement direction and movement amount of each point in the image, the accuracy of the estimation of the polyp size can be improved by using the optical flow.

In the estimation of the polyp size, the accuracy of the estimation of the polyp size can be improved by considering the manufacturer of the endoscope, the type of light source used by the endoscope, whether or not the endoscope performs enlarged display, and the like.FIG.7is a block diagram showing a functional configuration of an image processing device1bof the modification 2. As shown, in the modification 2, an image characteristic acquisition unit29is provided on the image processing device1b.The image characteristic acquisition unit29acquires image characteristic information such as the manufacturer of the endoscope, the type of light source used by the endoscope, whether or not the endoscope performs enlarged display, and the like. Specifically, the specification information of the endoscope may be used as the image characteristic information. In this case, the image characteristic acquisition unit29may read the specification information from a storage medium or the like that stores the specification information of the endoscope. Instead, the image characteristic acquisition unit29may estimate the image quality such as the brightness and the color of the image by analyzing the endoscopic image generated by the image area recognition unit22, and use it as the image characteristic information. The image characteristic acquisition unit29is an example of an image characteristic acquisition means.

The image characteristic acquisition unit29outputs the extracted image characteristic information to the first estimation unit24. The first size estimation model used by the first estimation unit24is the learned model which is learned in advance so as to estimate the polyp size based on the image of the polyp area and the image characteristic information. The first estimation unit24estimates the polyp size based on the image of the polyp area inputted by the polyp detection unit23and the image characteristic information inputted by the image characteristic acquisition unit29. This makes it possible to improve the accuracy of the estimation of the polyp size.

Second Example Embodiment

Next, a second example embodiment of the present invention will be described. Since the configuration of the endoscopic examination system100and hardware configuration of the image processing device1xaccording to the second example embodiment are the same as those in the first example embodiment described above, the description thereof will not be repeated.

FIG.8is a block diagram showing a functional configuration of the image processing device1xaccording to the second example embodiment. The image processing device1xaccording to the second example embodiment is obtained by adding a second estimation unit25and an estimation result integration unit26to the image processing device1according to the first example embodiment.

The second estimation unit25estimates the polyp size from the endoscopic image generated by the image area recognition unit22using a second size estimation model. That is, the first size estimation model estimates the polyp size from the image of the polyp area, whereas the second size estimation model estimates the polyp size from the entire endoscopic image. The second size estimation model is the model which is learned in advance so as to estimate the size of the polyp included in the endoscopic image.

Specifically, first, the second estimation unit25resizes the endoscopic image generated by the image area recognition unit22to the suitable size for the second size estimation model. Next, the second estimation unit25detects the polyp using the second size estimation model prepared in advance and estimates whether or not the polyp is equal to or larger than the predetermined size. Similar to the first estimation unit24, the second estimation unit25calculates the score indicating the probability that the polyp included in the endoscopic image is equal to or larger than Xmm (large polyp score) and the score indicating the probability that the polyp included in the endoscopic image is smaller than Xmm (small polyp score), and outputs the score to the estimation result integration unit26.

The estimation result integration unit26integrates the estimation result of the polyp size by the first estimation unit24and the estimation result of the polyp size by the second estimation unit25. For example, regarding the size of a certain polyp, it is assumed that the first estimation unit24outputs the large polyp score “0.6” and the small polyp score “0.4”, and the second estimation unit25outputs the large polyp score “0.8” and the small polyp score “0.2”. First, the estimation result integration unit26calculates the average value of the estimation results of the first estimation unit24and the second estimation unit25. In this example, the estimation result integration unit26calculates the average value of the large polyp score as “0.7” and the average value of the small polyp score as “0.3”. Then, the estimation result integration unit26compares the obtained average value with a predetermined threshold TH and outputs the estimation result of the polyp size. As described above, assuming that the threshold TH is “0.5”, the estimation result integration unit26estimates that the polyp is a large polyp, that is, the polyp size is Xmm or larger. Then, the estimation result integration unit26generates the display data Id based on the estimation result of the polyp size and the endoscopic image generated by the image area recognition unit22, and outputs the display data Id to the display device2.

As described above, in the second example embodiment, the second estimation unit25that estimates the polyp size from the entire endoscopic image is added to the configuration of the first example embodiment. Thus, if the polyp detection unit23misses the polyp, the second estimation unit25is possible to supplement it by detecting the polyp from the entire endoscope image and estimating the polyp size.

In the above-described configuration, the second estimation unit25is an example of a second estimation unit, and the estimation result integration unit26is an example of an estimation result integration means.

FIG.9is a flowchart of image display processing by the image processing device1xaccording to the second example embodiment. The image display processing flowchart according to the second example embodiment is obtained by adding the step S19˜S21to the image display processing flowchart according to the first example embodiment shown inFIG.4. Since the processing of step S11˜S18is the same as that in the first example embodiment, the description thereof will not be repeated.

The second estimation unit25acquires the endoscopic image42generated by the image area recognition unit22in the step S12. Then, the second estimation unit25resizes the endoscopic image42to the suitable size for the second size estimation model to generate the resized image46(step S19). Next, the second estimation unit25detects the polyp based on the resized image46using the second size estimation model, and outputs the estimation result of the polyp size to the estimation result integration unit26(step S20). The estimation result integration unit26calculates the average value of the size estimation result of the first estimation unit24and the size estimation result of the second estimation unit25(step S21), and determines the estimation result of the polyp size by comparing the average value with the threshold TH and displays it (step S22). Then, the processing ends.

Next, a modification of the second example embodiment will be described. The following modification can be applied to the second example embodiment.

In the second example embodiment, the first estimation unit24estimates the polyp size based on the image of the polyp area, and the second estimation unit25estimates the polyp size based on the endoscopic image. Here, the second estimation unit25may be configured to estimate the polyp size using the optical flow information. In this case, similarly to the modification 1 described above, the optical flow calculation unit28is provided on the image processing device1x,and the optical flow information calculated by optical flow calculation unit28is inputted into the second estimation unit25. The second size estimation model used by the second estimation unit25is the learned model which is learned in advance so as to estimate the polyp size based on the endoscopic image and the optical flow information.

Instead of the second estimation unit25, the first estimation unit24may be configured to estimate the polyp size using the optical flow information. In addition, both the first estimation unit24and the second estimation unit25may be configured to estimate the polyp size using the optical flow information. Furthermore, apart from the first estimation unit24and the second estimation unit25, an estimation unit for estimating the polyp size based on the endoscope image and the optical flow information may be provided. Thus, similarly to the modification 1, it is possible to improve the accuracy of the polyp size estimation using the optical flow information.

Also in the second example embodiment, in the estimation of the polyp size, the accuracy of the estimation of the polyp size can be improved by considering the manufacturer of the endoscope, the type of light source used by the endoscope, whether or not the endoscope performs enlarged display, and the like. In this case, similarly to modification 2 described above, the image characteristic acquisition unit29is provided on the image processing device1x,and the image characteristic information acquired by the image characteristic acquisition unit29is inputted to the second estimation unit25. The second size estimation model used by the second estimation unit25is the model which is learned in advance so as to estimate the polyp size based on the endoscopic image and the image characteristic information. Thus, similarly to the modification 2, it is possible to improve the accuracy of the polyp size estimation using the image characteristic information. Instead of the second estimation unit25, the first estimation unit24may be configured to estimate the polyp size using the image characteristic information. In addition, both the first estimation unit24and the second estimation unit25may be configured to estimate the polyp size using the image characteristic information.

Third Example Embodiment

FIG.10is a block diagram showing a functional configuration of an image processing device of a third example embodiment. The image processing device70includes an endoscopic image acquisition means71, a polyp detection means72, a first estimation means73, and an output means74.

FIG.11is a flowchart of processing by the image processing device of the third example embodiment. The endoscopic image acquisition means71acquires an endoscopic image (step S71). The polyp detection means72detects a polyp area from the endoscopic image (step S72). The first estimation means73estimates a size of the polyp based on an image of the detected polyp area (step S73). The output means74outputs an estimation result of the size of the polyp (step S74).

According to the image processing device70of the third example

embodiment, it is possible to detect the polyp during endoscopic examination and estimate the size of the polyp based on the image of the polyp area.

While the present disclosure has been described with reference to the example embodiments and examples, the present disclosure is not limited to the above example embodiments and examples. Various changes which can be understood by those skilled in the art within the scope of the present disclosure can be made in the configuration and details of the present disclosure.

An image processing device comprising:an endoscopic image acquisition means configured to acquire an endoscopic image;a polyp detection means configured to detect a polyp area from the endoscopic image;a first estimation means configured to estimate a size of the polyp based on an image of the detected polyp area; andan output means configured to output an estimation result of the size of the polyp.

The image processing device according to Supplementary note 1, wherein the first estimation means cuts out the image of the polyp area from the endoscope image and estimates the size of the polyp based on the image of the cut out polyp area, when the polyp detection means detects the polyp.

The image processing device according to Supplementary note 1, wherein the first estimation means performs drawing on the endoscope image so as to surround the polyp area and estimates the size of the polyp based on the surrounded polyp area, when the polyp detection means detects the polyp.

The image processing device according to any one of Supplementary notes 1 to 3, further comprising a display control means configured to display the endoscopic image and an estimation result of the size of the polyp on a display device,wherein the display control means superimposes and displays a figure surrounding the detected polyp area on the endoscopic image.

The image processing device according to any one of Supplementary notes 1 to 4, further comprising a calculation means configured to calculate optical flow information based on the endoscopic image,wherein the first estimation means estimates the size of the polyp based on the image of the polyp area and the optical flow information.

The image processing device according to any one of Supplementary notes 1 to 5, further comprising an image characteristic acquisition means configured to acquire image characteristic information of the endoscopic image,wherein the first estimation means estimates the size of the polyp based on the image of the polyp area and the image characteristic information.

The image processing device according to any one of Supplementary notes 1 to 6, further comprising a second estimation means configured to estimate the size of the polyp based on the endoscopic image; andan estimation result integration means configured to integrate the estimation result of the size of the polyp by the first estimation means and the estimation result of the size of the polyp by the second estimation means,wherein the output means outputs an integrated result by the estimation result integration means.

The image processing device according to Supplementary note 7, further comprising a calculation means configured to calculate optical flow information based on the endoscopic image,wherein the second estimation means estimates the size of the polyp based on the endoscope image and the optical flow information.

The image processing device according to any one of Supplementary notes 7 to 8, further comprising an image characteristic acquisition means configured to acquire image characteristic information of the endoscopic image,wherein the second estimation means estimates the size of the polyp based on the endoscope image and the image characteristic information.

An image processing method comprising:acquiring an endoscopic image;detecting a polyp area from the endoscopic image;estimating a size of the polyp based on an image of the detected polyp area; andoutputting an estimation result of the size of the polyp.

A recording medium storing a program, the program causing a computer to execute processing of:acquiring an endoscopic image;detecting a polyp area from the endoscopic image;estimating a size of the polyp based on an image of the detected polyp area; andoutputting an estimation result of the size of the polyp.

DESCRIPTION OF SYMBOLS