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arXiv · 1707.04931

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

Stefanos Apostolopoulos·Sandro De Zanet·Carlos Ciller·Sebastian Wolf·Raphael Sznitman
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Abstract

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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Stefanos Apostolopoulos, Sandro De Zanet, Carlos Ciller, Sebastian Wolf, Raphael Sznitman. 2017-07-16. Pathological OCT Retinal Layer Segmentation using Branch Residual U-shape Networks. https://arxiv.org/abs/1707.04931

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