Pathological OCT Retinal Layer Segmentation using Branch Residual U-shape Networks
This work addresses the challenge of segmenting highly warped retinal layers in pathological eyes, enabling better clinical monitoring of eye disorders.
The paper tackled automatic segmentation of retinal layers in OCT images for late-stage AMD patients, achieving higher performance and lower computational costs compared to state-of-the-art methods.
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.