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

arXiv: https://arxiv.org/abs/1707.04931 · HTML: https://mlsanity.com/paper/1707.04931/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2017.

## Built on by (most cited first, 5 of 5)

- [A comprehensive survey on recent deep learning-based methods applied to surgical data](https://mlsanity.com/paper/2209.01435/) (2022)
- [FourierNet: Shape-Preserving Network for Henle's Fiber Layer Segmentation in Optical Coherence Tomography Images](https://mlsanity.com/paper/2201.06435/) (2022)
- [Masked Image Modelling for retinal OCT understanding](https://mlsanity.com/paper/2405.14788/) (2024)
- [Lesion Localization in OCT by Semi-Supervised Object Detection](https://mlsanity.com/paper/2204.11227/) (2022)
- [Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations](https://mlsanity.com/paper/2403.15803/) (2024)

## Abstract (arXiv)

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.