Real-Time 3D Reconstruction of Colonoscopic Surfaces for Determining Missing Regions

Abstract

Colonoscopy is the most widely used medical technique to screen the human large intestine (colon) for cancer precursors. However, frequently parts of the surface are not visualized, and it is hard for the endoscopist to realize that from the video. Non-visualization derives from lack of orientations of the endoscope to the full circumference of parts of the colon, occlusion from colon structures, and intervening materials inside the colon. Our solution is real-time dense 3D reconstruction of colon chunks with display of the missing regions. We accomplish this by a novel deep-learning-driven dense SLAM (simultaneous localization and mapping) system that can produce a camera trajectory and a dense reconstructed surface for colon chunks (small lengths of colon). Traditional SLAM systems work poorly for the low-textured colonoscopy frames and are subject to severe scale/camera drift. In our method a recurrent neural network (RNN) is used to predict scale-consistent depth maps and camera poses of successive frames. These outputs are incorporated into a standard SLAM pipeline with local windowed optimization. The depth maps are finally fused into a global surface using the optimized camera poses. To the best of our knowledge, we are the first to reconstruct dense colon surface from video in real time and to display missing surface.