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

TL;DR

This paper introduces a novel CNN architecture with dilated residual blocks in an asymmetric U-shape design for accurate, efficient segmentation of retinal layers in highly pathological OCT images, aiding disease monitoring.

Contribution

The paper presents a new fully CNN model capable of segmenting multiple retinal layers in severely warped eyes in a single step, outperforming existing methods.

Findings

Lower computational costs compared to state-of-the-art methods.

Higher segmentation accuracy on late-stage AMD OCT images.

Effective in segmenting highly warped retinal layers.

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.