  Early diagnosis is essential for the successful treatment of bowel cancers
including colorectal cancer (CRC) and capsule endoscopic imaging with robotic
actuation can be a valuable diagnostic tool when combined with automated image
analysis. We present a deep learning rooted detection and segmentation
framework for recognizing lesions in colonoscopy and capsule endoscopy images.
We restructure established convolution architectures, such as VGG and ResNets,
by converting them into fully-connected convolution networks (FCNs), fine-tune
them and study their capabilities for polyp segmentation and detection. We
additionally use Shape from-Shading (SfS) to recover depth and provide a richer
representation of the tissue's structure in colonoscopy images. Depth is
incorporated into our network models as an additional input channel to the RGB
information and we demonstrate that the resulting network yields improved
performance. Our networks are tested on publicly available datasets and the
most accurate segmentation model achieved a mean segmentation IU of 47.78% and
56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp
detection, the top performing models we propose surpass the current state of
the art with detection recalls superior to 90% for all datasets tested. To our
knowledge, we present the first work to use FCNs for polyp segmentation in
addition to proposing a novel combination of SfS and RGB that boosts
performance
