Focus tracking method under digestive endoscope based on sequential feature learning

Abstract

The invention relates to the technical field of medical image processing, in particular to a focus tracking method under a digestive endoscope based on sequential feature learning, which comprises thefollowing steps: collecting lesion video clips as training samples, and constructing and training a tracking model based on a convolutional neural network, a long short-term memory network and an optical flow vector diagram; acquiring network model parameters, acquiring a digestive endoscopy real-time examination video, deframing the digestive endoscopy real-time examination video into pictures,calculating optical flow vector diagrams of two adjacent frames of images, loading a network structure and model parameters based on a convolutional neural network, a long short-term memory network and the optical flow vector diagrams, and calculating the area and position of a focus in real time. By means of the method, the area and the position of the focus can be tracked in real time in the digestive endoscopy process for endoscopy doctors to refer to, and the situation that the focus area is lost due to illumination, angles, shielding and other reasons in the examination process can be effectively prevented. The detection and tracking capacity of the focus under the digestive endoscopy can be improved, and the examination quality of the digestive endoscopy is effectively improved.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to a method for tracking lesions under a digestive endoscope based on time-series feature learning. Background technique [0002] The "World Cancer Report" pointed out that cancer is one of the leading causes of death in the world, and gastrointestinal tumors are one of the most common malignant tumors. In 2015, the number of patients with gastric cancer and colon cancer in my country was more than 1 million, and the death toll was nearly 700,000, accounting for 1 / 4 of the total number of cancer deaths. The root cause of malignant tumors endangering human health is the difficulty of early detection. If digestive tract tumors are diagnosed at an early stage, the 5-year survival rate of patients can be as high as 90%, and if they progress to the middle and late stages, the 5-year survival rate of patients is only 5-25%. Therefore, early diagnosis is an important str...

AI Technical Summary

Problems solved by technology

[0004] Based on the technical problems existing in the background technology, the present invention proposes a method for tracking lesions under digestive endoscopy based on time-series feature learning, which can track lesions from three dimensions: spatial features, velocity vectors, and time-series features. The characteristics of high accuracy solve the problem that the existing lesion tracking technology is greatly affected by illumination, angle changes and occlusion, and the accuracy is low

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