# Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images
## 2 Related Work  
Accurate segmentation of the OD and OC in retinal fundus images is a critical component for early glaucoma diagnosis. Various deep learning-based approaches have been proposed to achieve this, primarily aiming to capture the structural characteristics of the OD and OC while ensuring precise boundary segmentation and stable computation of clinical indicators such as the CDR. The most widely used baseline architecture is U-Net, introduced by [PERSON] et al. [3], which employs an encoder-decoder structure with skip connections to effectively recover fine structures in medical images. On the Drishti-GS dataset, U-Net achieved an IoU of 0.9190 and a Dice score of 0.9559 for OD, as well as an IoU of 0.7416 and a Dice score of 0.8383 for OC. While U-Net showed high accuracy for large structures like the OD, it performed relatively poorly for the smaller OC regions with low boundary contrast. To overcome these limitations, [PERSON] et al. [4] proposed Attention U-Net, which integrates an attention mechanism into U-Net to suppress irrelevant background information and enhance performance by focusing visual attention. [PERSON] et al. [5] and [PERSON] et al. [6] attempted to improve boundary delineation by employing FCN-based architectures with streamlined decoding strategies. [PERSON] et al. [7] and [PERSON] et al. [8] enhanced OD and OC segmentation performance by applying post-processing and boundary refinement techniques to the encoder's CNN features, achieving OD Dice scores of 0.9730 and OC Dice scores between 0.8500 and 0.8910. These studies demonstrated that optimization techniques could significantly improve performance even with simple network structures. More recently, there have been efforts to maximize performance through multi-scale feature integration and complex loss function designs. [PERSON] et al. [9] applied a Residual U-Net-based structure with specialized loss weighting, achieving an OD Dice score of 0.9741 and an OC Dice score of 0.8893. [PERSON] et al. [10] and [PERSON] et al. [11] reported outstanding performance with OD Dice scores of 0.9743 and 0.9746 and OC Dice scores of 0.9083 and 0.8992, respectively. These approaches particularly focused on capturing subtle variations in OC boundaries, achieving clinically meaningful results. However, these CNN-based approaches, relying primarily on local operations, still face limitations in fully capturing the structural inclusion relationship between the OD and OC and modeling global contextual information. To address these issues, Transformer-based models have recently gained attention [12; 13; 14]. Transformers can learn global information from images through the self-attention mechanism, enhancing the recognition of fine structures and boundary accuracy. The Vision Transformer (ViT) by [PERSON] et al. [15] processes images by splitting them into patches, and in addition to classification tasks, ViT-based segmentation models achieved a mean IoU (mIoU) of 47.6% on the ADE20K dataset, demonstrating performance comparable to or better than CNN-based models like ResNet. [PERSON] et al. [16] proposed CCNet, which introduced Criss-Cross Attention to reduce the computational complexity inherent in traditional Transformers by limiting interactions to pixels in the same rows and columns while still effectively capturing global contextual information. CCNet achieved an mIoU of 81.9% on the Cityscapes dataset, improving both efficiency and performance. [PERSON] et al. [17] introduced the Swin Transformer, which overcomes limitations of the ViT by adopting a hierarchical structure and a Shifted Window Attention mechanism. By applying local attention within windows and shifting the windows between layers, the Swin Transformer effectively captures global information while maintaining computational efficiency.