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

arXiv: https://arxiv.org/abs/2102.04799 · HTML: https://mlsanity.com/paper/2102.04799/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2021.

## Built on by (most cited first, 14 of 14)

- [Y-Net: A Spatiospectral Dual-Encoder Networkfor Medical Image Segmentation](https://mlsanity.com/paper/2204.07613/) (2022)
- [Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey](https://mlsanity.com/paper/2405.01636/) (2024)
- [Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotations](https://mlsanity.com/paper/2312.01726/) (2023)
- [MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis](https://mlsanity.com/paper/2506.08900/) (2025)
- [Light-weight Retinal Layer Segmentation with Global Reasoning](https://mlsanity.com/paper/2404.16346/) (2024)
- [Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review](https://mlsanity.com/paper/2109.02322/) (2021)
- [Formula-Driven Data Augmentation and Partial Retinal Layer Copying for Retinal Layer Segmentation](https://mlsanity.com/paper/2410.01185/) (2024)
- [Universal Wavelet Units in 3D Retinal Layer Segmentation](https://mlsanity.com/paper/2507.16119/) (2025)
- [Multi-scale Sparse Representation-Based Shadow Inpainting for Retinal OCT Images](https://mlsanity.com/paper/2202.11377/) (2022)
- [3D PETCT Tumor Lesion Segmentation via GCN Refinement](https://mlsanity.com/paper/2302.12571/) (2023)
- [Text-Driven Weakly Supervised OCT Lesion Segmentation with Structural Guidance](https://mlsanity.com/paper/2411.12615/) (2024)
- [TAPE: A two-stage parameter-efficient adaptation framework for foundation models in OCT-OCTA analysis](https://mlsanity.com/paper/2604.04571/) (2026)
- [LUMOS: Universal Semi-Supervised OCT Retinal Layer Segmentation with Hierarchical Reliable Mutual Learning](https://mlsanity.com/paper/2604.05388/) (2026)
- [Spatial Normalization for Cross-Domain Retinal Layer Segmentation in Optical Coherence Tomography](https://mlsanity.com/paper/2607.16065/) (2026)

## Abstract (arXiv)

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±0.001 and the pixel accuracy is 0.830±0.002, both of which outperform those from other state-of-the-art techniques.