  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.
