MGU-Net

Multi-Scale GCN-Assisted Two-Stage Network for Joint Segmentation of Retinal Layers and Disc in Peripapillary OCT Images

The codes are implemented in PyTorch and trained on NVIDIA Tesla V100 GPUs.

Introduction

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 develop 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 conduct experiments on human peripapillary retinal OCT images. We also provide public access to the collected dataset, which might contribute to the research in the field of biomedical image processing. 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.

Experiments

Dataset

1. Collected dataset: Download our collected dataset.
   The labeled images are grayscale images. Labels and corresponding pixel values are as follows:
   RNFL=26, GCL=51, IPL=77, INL=102, OPL=128, ONL=153, IS/OS=179, RPE=204, Choroid=230, Optic Disc=255

2. Public dataset: Download Duke SD-OCT dataset

Train and test

Run the following script to train and test our model.

python main_ts.py --name tsmgunet -d ./data/dataset --batch-size 1 --epoch 50 --lr 0.001

Results

Results on the collected dataset

Results on the public dataset

For more details, please refer to our paper.

Citation

If you use the codes or collected dataset for your research, please cite the following paper:

@article{li2021mgunet,
author = {Jiaxuan Li and Peiyao Jin and Jianfeng Zhu and Haidong Zou and Xun Xu and Min Tang and Minwen Zhou and Yu Gan and Jiangnan He and Yuye Ling and Yikai Su},
journal = {Biomed. Opt. Express},
number = {4},
pages = {2204--2220},
title = {Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and discs in peripapillary OCT images},
volume = {12},
year = {2021},
url = {http://www.osapublishing.org/boe/abstract.cfm?URI=boe-12-4-2204},
doi = {10.1364/BOE.417212},
}

Acknowledgements

The codes are built on AI-Challenger-Retinal-Edema-Segmentation and GloRe. We sincerely appreciate the authors for sharing their codes.

Contact

If you have any questions, please do not hesitate to contact jiaxuan.li@sjtu.edu.cn