# A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

Source: [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](<https://www.sota2.com/research/paper/68fa62fe1e209131ce9fc0ea>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:30:18.740Z

Authors: David V\\'azquez, Jorge Bernal, F. Javier S\\'anchez, Gloria Fern\\'andez-Esparrach, Antonio M. L\\'opez, Adriana Romero, Michal Drozdzal, Aaron Courville

Published: 2016-12-02T19:25:44.000Z

## Abstract

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. We provide new baselines on this dataset by training standard fully convolutional networks (FCN) for semantic segmentation and significantly outperforming, without any further post-processing, prior results in endoluminal scene segmentation.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1612.00799>)

[PDF](<https://arxiv.org/pdf/1612.00799.pdf>)