Open AccessGraph convolutional network based optic disc and cup segmentation on fundus images
Author(s) -
Zhiqiang Tian,
Yaoyue Zheng,
Xiaojian Li,
Shaoyi Du,
Xiayu Xu
Publication year - 2020
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.390056
Subject(s) - jaccard index , computer science , artificial intelligence , convolutional neural network , pattern recognition (psychology) , optic cup (embryology) , segmentation , graph , feature (linguistics) , feature extraction , extractor , biochemistry , chemistry , linguistics , philosophy , theoretical computer science , process engineering , engineering , eye development , gene , phenotype
Calculating the cup-to-disc ratio is one of the methods for glaucoma screening with other clinical features. In this paper, we propose a graph convolutional network (GCN) based method to implement the optic disc (OD) and optic cup (OC) segmentation task. We first present a multi-scale convolutional neural network (CNN) as the feature map extractor to generate feature map. The GCN takes the feature map concatenated with the graph nodes as the input for segmentation task. The experimental results on the REFUGE dataset show that the Jaccard index (Jacc) of the proposed method on OD and OC are 95.64% and 91.60%, respectively, while the Dice similarity coefficients (DSC) are 97.76% and 95.58%, respectively. The proposed method outperforms the state-of-the-art methods on the REFUGE leaderboard. We also evaluate the proposed method on the Drishthi-GS1 dataset. The results show that the proposed method outperforms the state-of-the-art methods.