# Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images
## 5 Results and Analysis
### Ablation Study
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Aug**} & \multirow{2}{*}{**Geo. Loss**} & \multirow{2}{*}{**BAT**} & \multicolumn{2}{c}{**REFUGE**} & \multicolumn{2}{c}{**G1020**} & \multicolumn{2}{c}{**ORIGA**} \\ \cline{5-10}  & & & & **IoU** & **Dice** & **IoU** & **Dice** & **IoU** & **Dice** \\ \hline Baseline & - & - & - & 0.9171 & 0.9177 & 0.9097 & 0.9068 & 0.9045 & 0.9164 \\ Baseline + & ✓ & - & - & 0.9247 & 0.9353 & 0.9157 & 0.9244 & 0.9221 & 0.9340 \\  & - & ✓ & - & 0.9302 & 0.9396 & 0.9237 & 0.9389 & 0.9368 & 0.9415 \\  & - & - & ✓ & 0.9468 & 0.9574 & 0.9378 & 0.9465 & 0.9442 & 0.9561 \\  & ✓ & ✓ & - & 0.9365 & 0.9471 & 0.9275 & 0.9362 & 0.9339 & 0.9458 \\  & - & ✓ & ✓ & 0.9651 & 0.9673 & 0.9478 & 0.9564 & 0.9541 & 0.9660 \\  & ✓ & - & ✓ & 0.9567 & 0.9681 & 0.9581 & 0.9691 & 0.9672 & 0.9784 \\
**Proposed** & ✓ & ✓ & ✓ & **0.9786** & **0.9892** & **0.9696** & **0.9783** & **0.9760** & **0.9879** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study results on key components of the proposed model. Figure 17 presents a visual comparison of the ROI-based ablation study results on the REFUGE and ORIGA datasets. Each row corresponds to a different patient's fundus image, and each column shows the output of a specific model configuration. In the baseline model, the OD boundary appears irregular and jagged, and the segmentation error is especially pronounced in the OC region. This is likely due to the model's inability to capture structural relationships or encode boundary-specific information effectively. When data augmentation was added, the boundary irregularities around the OD persisted, but the OC prediction showed noticeable improvement. This indicates that the truncated Gaussian-based augmentation and colormap transformations helped emphasize structural features in the fundus images, contributing to better OC localization. From the point where Geometry-aware Loss was applied, the outer OD boundary became smoother, and much of the jaggedness was corrected. The precision of OC segmentation also improved significantly. This demonstrates that incorporating the Hausdorff Distance into the loss function made a substantial contribution to boundary learning, enabling the model to reduce structural errors more effectively. Figure 17: Ablation study results for the proposed model. (**a**) original image, (**b**) ground truth (GT) mask, (**c**) baseline model, (**d**) baseline + data augmentation, (**e**) baseline + Geometry-aware Loss, (**f**) baseline + BAT, and (**g**) proposed model. When BAT was applied, the overall shapes of both the OD and OC boundaries closely resembled the ground truth, although small deviations remained. The segmentation results clearly reflected the hierarchical relationship between the OD and OC, confirming that BAT effectively captures global context and structural containment between the two regions. Finally, the proposed model, which integrates data augmentation, Geometry-aware Loss, and BAT, produced segmentation results that were nearly indistinguishable from the ground truth. The boundaries of both the OD and OC were smooth, consistent, and accurately located. This outcome illustrates how the synergistic combination of all three components leads to significant improvements in both boundary precision and structural consistency. Overall, these visual results validate that while each component contributes to performance in a distinct yet complementary manner, their integration in the full model enables it to deliver predictions that most closely resemble the ground truth, thereby confirming the effectiveness of the proposed approach.