A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

TL;DR

This paper introduces a new benchmark dataset for colonoscopy image segmentation to improve decision support systems, demonstrating that standard FCNs significantly outperform previous methods in endoluminal scene segmentation.

Contribution

The paper provides an extended colonoscopy image benchmark and establishes new baseline results using fully convolutional networks for segmentation.

Findings

New benchmark dataset for colonoscopy image analysis.

Standard FCNs outperform previous methods without post-processing.

Improved accuracy in endoluminal scene segmentation.

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.