Lighting Enhancement Aids Reconstruction of Colonoscopic Surfaces
This work addresses a domain-specific problem for colonoscopy screening by improving real-time 3D reconstruction to aid doctors in detecting unsurveyed regions, though it is incremental as it focuses on enhancing an existing system component.
The paper tackled lighting inconsistency in colonoscopy videos, which causes failures in 3D surface reconstruction systems, by developing a real-time RNN-based lighting correction method that adapts gamma values to recent frames, significantly boosting reconstruction success rates and quality.
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.