Paper Info
Title
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images.
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 the 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 segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene, targeting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCNs). We perform a comparative study to show that FCNs significantly outperform, without any further postprocessing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.
Year
DOI
Venue
2016
10.1155/2017/4037190
JOURNAL OF HEALTHCARE ENGINEERING
Field
DocType
Volume
Data mining,Colonoscopy,Image segmentation,Artificial intelligence,Medicine,Benchmarking,Computer vision,Segmentation,Decision support system,Visual assessment,Colorectal cancer,Scene segmentation,Machine learning
Journal
2017
ISSN
Citations 
PageRank 
2040-2295
12
0.53
References 
Authors
5
8
Name
Order
Citations
PageRank
David Vázquez148828.04
Jorge Bernal217110.51
F. Javier Sánchez3465.05
Gloria Fernández-Esparrach4372.28
Antonio M. López573954.13
Adriana Romero642930.57
Michal Drozdzal7391.62
Aaron C. Courville86671348.46