Detecting Deficient Coverage in Colonoscopies

Abstract

Colonoscopy is the tool of choice for preventing Colorectal Cancer, by detecting and removing polyps before they become cancerous. However, colonoscopy is hampered by the fact that endoscopists routinely miss 22-28% of polyps. While some of these missed polyps appear in the endoscopist's field of view, others are missed simply because of substandard coverage of the procedure, i.e. not all of the colon is seen. This paper attempts to rectify the problem of substandard coverage in colonoscopy through the introduction of the C2D2 (Colonoscopy Coverage Deficiency via Depth) algorithm which detects deficient coverage, and can thereby alert the endoscopist to revisit a given area. More specifically, C2D2 consists of two separate algorithms: the first performs depth estimation of the colon given an ordinary RGB video stream; while the second computes coverage given these depth estimates. Rather than compute coverage for the entire colon, our algorithm computes coverage locally, on a segment-by-segment basis; C2D2 can then indicate in real-time whether a particular area of the colon has suffered from deficient coverage, and if so the endoscopist can return to that area. Our coverage algorithm is the first such algorithm to be evaluated in a large-scale way; while our depth estimation technique is the first calibration-free unsupervised method applied to colonoscopies. The C2D2 algorithm achieves state of the art results in the detection of deficient coverage. On synthetic sequences with ground truth, it is 2.4 times more accurate than human experts; while on real sequences, C2D2 achieves a 93.0% agreement with experts.

FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos

“…Deep learning approaches have recently shown promising results in various endoscopic intervention tasks such as depth estimation, 3D reconstruction, and surface coverage [2,6,11,14]. Deep learning models are data driven and the supervised category requires ground truth information.…”

Visualizing Missing Surfaces In Colonoscopy Videos using Shared Latent Space Representations

“…Recently, Ma et al [5] and Freedman et al [6] have presented approaches to quantify colon surface coverage. Ma et al [5] reconstruct 3D mesh from contiguous chunk of colonoscopy video frames using training data generated from shape-from-motion.…”

“…The method, however, assumes cylindrical topology (endoluminal) views, smooth camera movements and masked-out specular reflection, making the method less practical in general colonoscopy scenarios. In contrast, Freedman et al [6] have used a deep learning ap-proach to estimate percentage coverage value directly for given colonoscopy video segments but do not provide any means for visualizing the missed colon surface.…”