2021/03/18 by Yubo Zhang, Zhang, Yubo, Shuxian Wang +11
Computer Science · Engineering · Medicine · #Advanced Image and Video Retrieval Techniques #Advanced Vision and Imaging #Artificial intelligence #Cancer #Colonoscopy #Colorectal cancer #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Consistency (knowledge bases) #FOS: Computer and information sciences #Focus (optics) #Internal medicine #Machine Learning (cs.LG) #Medicine #Optics #Physics #Robotics and Sensor-Based Localization #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2103.10310
published in arXiv (Cornell University) (Cornell University) · Accepted at IPMI 2021 (The 27th international conference on Information Processing in Medical Imaging)
arxiv created 2021/03/18 · openalex publication_date 2021/03/18 · arxiv updated 2021/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
High screening coverage during colonoscopy is crucial to effectively prevent colon cancer. Previous work has allowed alerting the doctor to unsurveyed regions by reconstructing the 3D colonoscopic surface from colonoscopy videos in real-time. However, the lighting inconsistency of colonoscopy videos can cause a key component of the colonoscopic reconstruction system, the SLAM optimization, to fail. In this work we focus on the lighting problem in colonoscopy videos. To successfully improve the lighting consistency of colonoscopy videos, we have found necessary a lighting correction that adapts to the intensity distribution of recent video frames. To achieve this in real-time, we have designed and trained an RNN network. This network adapts the gamma value in a gamma-correction process. Applied in the colonoscopic surface reconstruction system, our light-weight model significantly boosts the reconstruction success rate, making a larger proportion of colonoscopy video segments reconstructable and improving the reconstruction quality of the already reconstructed segments.