Depth Reconstruction and Computer-Aided Polyp Detection in Optical Colonoscopy Video Frames
This work addresses the issue of polyp miss rates in colon cancer screening, which affects millions of patients annually, but it appears incremental as it builds on existing detection methods with specific improvements.
The paper tackles the problem of polyp detection in optical colonoscopy images, which have a high miss rate, by developing an automatic detection algorithm that uses depth maps and polyp profiles, achieving a recall of 84.0% and specificity of 83.4%.
We present a computer-aided detection algorithm for polyps in optical colonoscopy images. Polyps are the precursors to colon cancer. In the US alone, more than 14 million optical colonoscopies are performed every year, mostly to screen for polyps. Optical colonoscopy has been shown to have an approximately 25% polyp miss rate due to the convoluted folds and bends present in the colon. In this work, we present an automatic detection algorithm to detect these polyps in the optical colonoscopy images. We use a machine learning algorithm to infer a depth map for a given optical colonoscopy image and then use a detailed pre-built polyp profile to detect and delineate the boundaries of polyps in this given image. We have achieved the best recall of 84.0% and the best specificity value of 83.4%.