Detecting Deficient Coverage in Colonoscopies

Abstract

Colonoscopy is 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-bysegment 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.

Unsupervised Monocular Depth Estimation for Colonoscope System Using Feedback Network

“…In the graphics engine, animation scenes are created by changing textures, creating virtual camera paths, and using various lights. The image and depth pairs to be used as the synthetic dataset are the outputs of each image and depth renderer in the produced animation scene [6,14].…”

“…Unlike the supervised method, which requires data consisting of pairs of image and depth, the unsupervised depth estimation network uses continuous colonoscopy images as training data. Therefore, the self-supervised method uses not only synthetic datasets, but also images taken from real patients or images from phantoms for network training [6,26].…”

“…Endoscopy doctors' fatigue and skill problems can be compensated for by artificial intelligence and robotic medical systems [3]. Recently, polyp detection [4], size classification [5], and detecting deficient coverage in colonoscopy [6] have been proposed as computer-assisted technologies using artificial intelligence. In the field of robotic colonoscopy technology, there are studies on conventional colonoscope miniaturizing [3], robotic meshworm [7], treaded capsule [8], and autonomous locomotion system [9] to facilitate colonoscopy.…”

“…In the study of colonoscopy depth estimation using a monocular supervised learning method [13][14][15], conditional random field, pix2pix [18], and a conditional generative adversarial network (GAN) [19] were used as the depth prediction network. In the study of measuring the coverage of colonoscopy based on a self-supervised learning [6], the view synthesis loss [20] and the prediction of the camera intrinsic matrix in the network [21] are applied. However, the depth obtained Sensors 2021, 21, 2691 2 of 16 by the monocular learning-based method often flickers depending on the scale ambiguity and prediction per single frame [22].…”

Artificial intelligence for colonoscopic polyp detection: High performance versus human nature

“…The technical challenges to this are significantly greater than polyp segmentation in a video image but would mean that both components that we think define a high‐quality examination, and that should result in the lowest PCCRC rate, would be met. Google and other groups are already working on this problem 9 …”