Real-time endoscope enteroscope polyp detection system

Abstract

The invention discloses a real-time endoscope enteroscope polyp detection system which comprises a video information acquisition unit which is used for decomposing a received video to video segments in which a plurality of frames form a group; a video information extraction unit which is used for preprocessing the video clips and extracting optical flow information; and a video information analysis unit which is used for acquiring the video information processed by the video information extraction unit and inputting the video information into the deep convolutional neural network for detectionso as to acquire a polyp detection result. By means of the real-time endoscope enteroscope polyp detection system, space-time information can be extracted from video streams generated in real time inthe operation process, a doctor is assisted in discovering intestinal polyp with low delay and high precision, the judgment precision of the doctor is improved, the operation burden is relieved, andthe operation process is accelerated.

Description

technical field [0001] The application relates to an intestinal polyp detection system, in particular to a real-time endoscopic colonoscopy polyp detection system combining single-frame color images and multi-frame optical flow image spatio-temporal information, which belongs to the field of medical technology. Background technique [0002] Colorectal polyps are abnormal growths that start in the lining of the colon or rectum. While most polyps are safe, some carry a risk of becoming cancerous and leading to colorectal cancer. Colorectal cancer is currently the fourth most common cancer with the second highest mortality worldwide. At present, more than half of the people over the age of 60 have more than one colorectal polyp, so the early detection and removal of polyps is very important for the prevention of colorectal cancer. Endoscopic colonoscopy is the most common and effective preventive method to discover and remove polyps, but manual diagnosis is limited by the expe...

AI Technical Summary

Problems solved by technology

[0007] First, it is non-real-time. Due to its complexity, the speed of this method cannot exceed 30 frames per second on existing equipment, so it cannot be used for real-time purposes, and can only be used for postoperative analysis

[0008] Second, low accuracy and sensitivity. Most deep learning models require a large amount of accurately labeled data for training, but the polyp labeling task is limited by medical expertise. Most public data sets have the problem of small numbers and few types. Therefore, the training results are unsatisfactory

At the same time, because detection methods based on single-frame information are often trapped in the complex environment of the intestinal tract, there are a lot of noise such as foreign objects, liquids, and blurs in the field of view of the lens, it is difficult to make an accurate result evaluation based on a single image. There are reasons why the accuracy and sensitivity of the method are not satisfactory

[0009] In order to reduce noise interference, some researchers introduce time information to analyze consecutive multiple frames in the video. At present, there are three methods that have been published in the academic field, namely convolution long short-term memory neural network (ConvLSTM), conditional random field (CRF) Combined with deep learning-based target tracking methods to fuse multi-frame detection results, but these methods are limited by model complexity and cannot achieve real-time detection

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