  The automatic segmentation of retinal layer structures enables
clinically-relevant quantification and monitoring of eye disorders over time in
OCT imaging. Eyes with late-stage diseases are particularly challenging to
segment, as their shape is highly warped due to pathological biomarkers. In
this context, we propose a novel fully Convolutional Neural Network (CNN)
architecture which combines dilated residual blocks in an asymmetric U-shape
configuration, and can segment multiple layers of highly pathological eyes in
one shot. We validate our approach on a dataset of late-stage AMD patients and
demonstrate lower computational costs and higher performance compared to other
state-of-the-art methods.
