# Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images
## 5 Results and Analysis
### Ablation Study  
In this study, we conducted an ablation study to evaluate the individual and combined contributions of the proposed BAT, Geometry-aware Loss, and data augmentation techniques to the overall model performance. Table 3 presents the results of this analysis,  
Figure 16: Segmentation results on ROI images from the G1020 dataset. (**a**) ROI images, (**b**) ROI ground truth, (**c**) predictions mask, and (**d**) predictions from the proposed model with attention maps. showing how the IoU and Dice coefficients changed across three benchmark datasets--REFUGE, G1020, and ORIGA --as each component was incrementally integrated into the model. This study conducted an ablation experiment to independently analyze the contribution of the proposed structure-preserving data augmentation, BAT module, and Geometry-aware Loss to model performance and to verify the synergistic effect when these components are combined. The baseline model was set as a standard U-Net without data augmentation, Geometry-aware Loss, or BAT applied. In this configuration, it achieved performances of an IoU of 0.9171 and a Dice score of 0.9177 on REFUGE, an IoU of 0.9097 and a Dice score of 0.9068 on G1020, and an IoU of 0.9045 and a Dice score of 0.9164 on ORIGA. These results show that a basic U-Net can perform segmentation to a certain extent, but limitations exist in boundary precision. Applying truncated Gaussian sampling and colormap transformation improved the performance on the REFUGE dataset, with the IoU increasing from 0.9171 to 0.9247 and the Dice coefficient from 0.9177 to 0.9353. This suggests that the increased data diversity contributed to enhanced boundary recognition performance. However, data augmentation alone was insufficient to overcome limitations in structural relationship learning and boundary precision. When Geometry-aware Loss, incorporating the normalized Hausdorff Distance, was applied to the baseline, the performance improved to an IoU of 0.9302 and a Dice score of 0.9396 on the REFUGE dataset. Notably, the Dice coefficient showed a larger improvement, indicating enhanced segmentation precision around the boundaries. Applying only the BAT module to the baseline resulted in an IoU of 0.9468 and a Dice coefficient of 0.9574 on REFUGE, demonstrating that by learning the structural inclusion relationship between the OD and OC, the model was able to recognize boundaries more clearly. The greater improvement in the Dice score compared to the IoU supports the effectiveness of BAT in specifically enhancing boundary precision. Finally, the full model integrating all components (data augmentation + Geometry-aware Loss + BAT) achieved the highest performance, with REFUGE scores of an IoU of 0.9786 and a Dice score of 0.9892, G1020 scores of an IoU of 0.9696 and a Dice score of 0.9783, and ORIGA scores of an IoU of 0.976 and a Dice score of 0.9879. Although significant performance improvements were observed when only combining Geometry-aware Loss and BAT, the full model with additional data augmentation achieved even higher precision. This demonstrates that each component contributes to different aspects--enhancing data diversity, strengthening boundary recognition, and preserving structural information--and that their combination yields a synergistic effect. In summary, the ablation study empirically confirms that the three proposed components each independently lead to meaningful performance improvements and act complementarily to maximize the model's boundary recognition and structural preservation capabilities.