DST-Net: Dual Self-Integrated Transformer Network for Semi-Supervised Segmentation of Optic Disc and Optic Cup in Fundus Image

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. Firstly, we 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. Secondly, we employed a dual self-integrated teacher-student network with a substantial amount of unlabeled data, which is utilized through semi-supervised learning to facilitate the acquisition of more refined segmentation outcomes. Finally, the Boundary Difference over Union Loss (BDoU-loss) enhances the network's focus on the boundaries. We implemented the comparative experiments on the publicly available dataset RIGA+. The Dice value of OD and OC of the proposed DST-Net reached 95.12(±)0.14 and 85.69(±)0.27, respectively, outperforming other State-Of-The-Art (SOTA) methods and proving its promising prospect in OD and OC segmentation.