# Detecting Deficient Coverage in Colonoscopies

arXiv: https://arxiv.org/abs/2001.08589 · HTML: https://mlsanity.com/paper/2001.08589/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2020.

## Built on by (most cited first, 14 of 14)

- [Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registration](https://mlsanity.com/paper/2206.08903/) (2022)
- [Bimodal Camera Pose Prediction for Endoscopy](https://mlsanity.com/paper/2204.04968/) (2022)
- [SimCol3D -- 3D Reconstruction during Colonoscopy Challenge](https://mlsanity.com/paper/2307.11261/) (2023)
- [LightDepth: Single-View Depth Self-Supervision from Illumination Decline](https://mlsanity.com/paper/2308.10525/) (2023)
- [On the Uncertain Single-View Depths in Colonoscopies](https://mlsanity.com/paper/2112.08906/) (2021)
- [FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos](https://mlsanity.com/paper/2106.12522/) (2021)
- [Task-guided Domain Gap Reduction for Monocular Depth Prediction in Endoscopy](https://mlsanity.com/paper/2310.01663/) (2023)
- [Visualizing Missing Surfaces In Colonoscopy Videos using Shared Latent Space Representations](https://mlsanity.com/paper/2101.07280/) (2021)
- [Leveraging a realistic synthetic database to learn Shape-from-Shading for estimating the colon depth in colonoscopy images](https://mlsanity.com/paper/2311.05021/) (2023)
- [ColonNeRF: High-Fidelity Neural Reconstruction of Long Colonoscopy](https://mlsanity.com/paper/2312.02015/) (2023)
- [ColNav: Real-Time Colon Navigation for Colonoscopy](https://mlsanity.com/paper/2306.04269/) (2023)
- [Estimating the coverage in 3d reconstructions of the colon from colonoscopy videos](https://mlsanity.com/paper/2210.10459/) (2022)
- [SoftEnNet: Symbiotic Monocular Depth Estimation and Lumen Segmentation for Colonoscopy Endorobots](https://mlsanity.com/paper/2301.08157/) (2023)
- [Colonoscopy Coverage Revisited: Identifying Scanning Gaps in Real-Time](https://mlsanity.com/paper/2305.10026/) (2023)

## Abstract (arXiv)

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.