# Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images
## 5 Results and Analysis
### Performance Analysis on Different Datasets
DRIONS-DB is a dataset that provides manual annotations only for the OD region, without any segmentation labels for the OC. Accordingly, segmentation performance on this dataset was evaluated solely based on the OD region. DRIONS-DB is known for its challenging characteristics, particularly due to the faint and unclear OD boundaries, which often hinder the performance of conventional models. Existing methods tend to produce blurred segmentation masks, struggling to localize the OD boundary accurately. In contrast, the proposed model equipped with BAT achieved  
Figure 8: Segmentation results on ROI images from the DRIONS-DB dataset. (**a**) ROI images, (**b**) ROI ground truth, (**c**) predictions mask, and (**d**) predictions from the proposed model with attention maps. Figure 7: Segmentation results on fundus images from the DRIONS-DB dataset. (**a**) input fundus images, (**b**) ground truth, (**c**) predictions mask, and (**d**) predictions from the proposed model with attention maps. significantly superior results, recording an IoU of 0.9758 and a Dice score of 0.9877. These results outperformed the best-performing baseline model, Swin-UNet, which achieved an IoU of 0.9631 and a Dice score of 0.9677--representing an improvement of 1.27% in IoU and 2.06% in Dice. This performance gain can be attributed to BAT's ability to more precisely capture boundary information, enabling the network to distinguish subtle structural edges with higher fidelity. As illustrated in Figures 7 and 8, the proposed model shows a clearer separation of boundaries compared to existing models both on full fundus images and on ROI visualizations. The Drishti-GS dataset provides separate annotations for both the OD and OC, enabling a detailed evaluation of segmentation performance for each structure. Due to the relatively small dataset size, there is potential for limited model generalization during training. Additionally, in some images, the boundaries between the OD and OC are visually ambiguous, leading existing segmentation models to produce incomplete or imprecise boundary predictions. Despite these challenges, the proposed model achieved the highest performance on the Drishti-GS dataset. Specifically, for OD segmentation, it recorded an IoU of 0.9756 and a Dice score of 0.9783. For the more challenging OC region, the model achieved an IoU of 0.8405 and a Dice score of 0.9127, surpassing the performance of the best existing model, Swin-UNet, which obtained an IoU of 0.8305 and a Dice score of 0.9027. These results demonstrate that the BAT enables the model to maintain strong generalization performance even on small-scale datasets, effectively enhancing boundary precision. As shown in Figures 9 and 10, the proposed model delivers clearer shape and contour delineation in both full fundus and ROI-based visualizations, supporting its robustness in structural segmentation. The REFUGE dataset, comprising a large number of images collected under various clinical conditions, is well suited for evaluating both the learning capacity and generalization ability of segmentation models. Its diversity makes it particularly valuable for assessing model robustness and applicability in real-world scenarios. The proposed model achieved an IoU of 0.9786 and a Dice score of 0.9892, outperforming the previous best model, Swin-UNet, with an improvement of approximately 1.75% in the IoU and 1.72% in the Dice score. These results highlight the effectiveness of the proposed model in learning boundary-specific features, even under varied imaging conditions. As shown in Figures 11 and 12, the proposed model delivers sharper boundary delineation in both full fundus images and ROI regions. Moreover, the attention maps  
Figure 12: Segmentation results on ROI images from the REFUGE dataset. (**a**) ROI images, (**b**) ROI ground truth, (**c**) predictions mask, and (**d**) predictions from the proposed model with attention maps.