Pathological OCT Retinal Layer Segmentation using Branch Residual U-style Networks

Apostolopoulos, Stefanos; De Zanet, Sandro; Ciller, Carlos; Wolf, Sebastian; Sznitman, Raphael (16 July 2017). Pathological OCT Retinal Layer Segmentation using Branch Residual U-style Networks. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI).

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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.

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