Work

DST-Net: Dual self-integrated transformer network for semi-supervised segmentation of optic disc and optic cup in fundus image

Year: 2025

Type: article

Abstract: Accurate and efficient optic disc and cup segmentation from fundus images is significant for glaucoma screening. However, current neural network-based optic disc (OD) and optic cup (OC) segmentation tend to prioritize the image's local edge features, thus limiting their capacity to model long-term relationships, with errors in delineating the boundaries. To address this issue, we proposed a semi-supervised dual self-integrated transformer network (DST-Net) for joint segmentation of the OD and OC. First, we introduce a dual-view co-training mechanism to construct the encoder and decoder of the self-integrated network from the mutually enhanced feature learning modules of Vision Transformer (ViT) and convolutional neural networks (CNN), which are co-trained with dual views to learn the global and local features of the image adaptively. Moreover, we employ a dual self-integrated teacher-student framework, effectively utilizing large amounts of unlabeled fundus images through semi-supervised learning, thereby refining OD and OC segmentation results. Finally, we use a boundary difference over union loss (BDoU-loss) to optimize boundary prediction further. We implemented the comparative experiments on the publicly available dataset RIGA+. The OD and OC Dice values of the proposed DST-Net reached 95.12 ± 0.14 and 85.69 ± 0.27, respectively, outperforming other state-of-the-art (SOTA) methods. In addition, DST-Net shows strong generalization on the DRISHTI-GS1 and RIM-ONE-v3 datasets, proving its promising prospect in OD and OC segmentation.

Source: Electronic Research Archive

DOI: 10.3934/era.2025097

Authors: Yanxia Sun, Tianze Xu, Jing Wang, Jinke Wang

Institution: Harbin University of Science and Technology

Language: English

Topic: Retinal Imaging and Analysis

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,000