Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images

TL;DR

This paper introduces a novel multi-scale GCN-assisted two-stage neural network that improves the accuracy of joint segmentation of retinal layers and the optic disc in peripapillary OCT images, aiding glaucoma diagnosis.

Contribution

It presents a new framework combining GCNs and a multi-scale reasoning module within a U-shaped network for enhanced retinal and optic disc segmentation.

Findings

Achieved Dice score of 0.820 for segmentation

Attained pixel accuracy of 0.830

Outperformed state-of-the-art methods

Abstract

An accurate and automated tissue segmentation algorithm for retinal optical coherence tomography (OCT) images is crucial for the diagnosis of glaucoma. However, due to the presence of the optic disc, the anatomical structure of the peripapillary region of the retina is complicated and is challenging for segmentation. To address this issue, we developed a novel graph convolutional network (GCN)-assisted two-stage framework to simultaneously label the nine retinal layers and the optic disc. Specifically, a multi-scale global reasoning module is inserted between the encoder and decoder of a U-shape neural network to exploit anatomical prior knowledge and perform spatial reasoning. We conducted experiments on human peripapillary retinal OCT images. The Dice score of the proposed segmentation network is 0.820$\pm$0.001 and the pixel accuracy is 0.830$\pm$0.002, both of which outperform those from other state-of-the-art techniques.