  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.
