# Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review

arXiv: https://arxiv.org/abs/1609.01915 · HTML: https://mlsanity.com/paper/1609.01915/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2016.

## Built on by (most cited first, 6 of 6)

- [SinGAN-Seg: Synthetic training data generation for medical image segmentation](https://mlsanity.com/paper/2107.00471/) (2021)
- [NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy](https://mlsanity.com/paper/2104.11138/) (2021)
- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey](https://mlsanity.com/paper/2401.11734/) (2024)
- [SuPerPM: A Surgical Perception Framework Based on Deep Point Matching Learned from Physical Constrained Simulation Data](https://mlsanity.com/paper/2309.13863/) (2023)
- [Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy](https://mlsanity.com/paper/2104.01268/) (2021)

## Abstract (arXiv)

Video capsule endoscopy (VCE) is used widely nowadays for visualizing the gastrointestinal (GI) tract. Capsule endoscopy exams are prescribed usually as an additional monitoring mechanism and can help in identifying polyps, bleeding, etc. To analyze the large scale video data produced by VCE exams automatic image processing, computer vision, and learning algorithms are required. Recently, automatic polyp detection algorithms have been proposed with various degrees of success. Though polyp detection in colonoscopy and other traditional endoscopy procedure based images is becoming a mature field, due to its unique imaging characteristics detecting polyps automatically in VCE is a hard problem. We review different polyp detection approaches for VCE imagery and provide systematic analysis with challenges faced by standard image processing and computer vision methods.