Lighting Enhancement Aids Reconstruction of Colonoscopic Surfaces

TL;DR

This paper introduces a real-time lighting correction method using an RNN to adapt gamma correction in colonoscopy videos, significantly enhancing 3D surface reconstruction success and quality.

Contribution

It presents a novel RNN-based gamma correction approach tailored for real-time colonoscopy video enhancement, addressing lighting inconsistency issues.

Findings

Increased reconstruction success rate in colonoscopy videos.

Improved quality of reconstructed colonoscopic surfaces.

Enhanced robustness of SLAM optimization under variable lighting.

Abstract

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.