%0 Journal Article
%T A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
%A David Vazquez
%A Jorge Bernal
%A F. Javier Sanchez
%A Gloria Fernandez Esparrach
%A Antonio Lopez
%A Adriana Romero
%A Michal Drozdzal
%A Aaron Courville
%J Journal of Healthcare Engineering
%D 2017
%F David Vazquez2017
%O ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2940), last updated on Thu, 16 Feb 2023 12:08:31 +0100
%X Colorectal cancer (CRC) is the third cause of cancer death world-wide. 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) aim- ing 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 endolumninal scene, tar- geting 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 (FCN). We perform a compar- ative  study to show that  FCN significantly outperform, without any further post-processing, prior  results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.
%K Colonoscopy images
%K Deep Learning
%K Semantic Segmentation
%U https://doi.org/10.1155/2017/4037190
%U http://refbase.cvc.uab.es/files/vbs2017b.pdf
%P 2040-2295