Detection of Endoscope Withdrawal Time in Colonoscopy Videos

A colonoscopy is an endoscopic examination that visualizes the colon by inserting a camera, attached to a long, flexible tube, into the patient’s anus and up the rectum. One significant landmark of a colonoscopy is when the endoscope begins to withdraw after reaching the farthest point in its path, the cecum. In this paper, we demonstrate that the withdrawal point is closely related to the sighting of local anatomical features in the cecum, specifically the ileocecal valve. The withdrawal point allows us to determine the amount of time the endoscope spends withdrawing, which is an important indicator of the quality of a colonoscopy. In this paper, we present a colonoscopy video processing system that detects the withdrawal point in real time by using a convolutional neural network to classify between ileocecal valves and other images. The system then processes the raw classifier output to determine the withdrawal point. We collect a novel dataset of colonoscopy images and videos to train and evaluate the classifier, as well as to evaluate the video processing system. We explore a range of state-of-the-art classifier architectures, and our best model achieves 99.6% accuracy on the image-level dataset. Then, we provide human-readable insight into our classifier using class activation mapping and principle component analysis. Using this classifier and optimized parameters, our video processing system achieves 70.5% accuracy (± 10s) on the video-level dataset.