# Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images
## 5 Results and Analysis
### State-of-the-Art Comparison  
Table 4 presents a comparative evaluation of the segmentation performance between the proposed model and several state-of-the-art (SOTA) methods on three benchmark public datasets: DRIONS-DB, Drishti-GS, and REFUGE. The performance was assessed using the IoU values and Dice coefficients for both the OD and OC regions. On the DRIONS-DB dataset, the proposed model achieved an IoU of 0.9758 and a Dice score of 0.9877, outperforming the best existing method by [25] (IoU of 0.9474, Dice score of 0.9768) by 2.84 percentage points in the IoU and 1.09 points in the Dice score. This significant improvement indicates that the proposed BAT effectively enhances the model's ability to learn precise OD boundaries, while the incorporation of Geometry-aware Loss substantially contributes to boundary refinement and segmentation precision. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Database**} & \multirow{2}{*}{**Author**} & \multicolumn{2}{c}{**OD**} & \multicolumn{2}{c}{**OC**} \\ \cline{3-6}  & & **IoU** & **Dice** & **IoU** & **Dice** \\ \hline \multirow{8}{*}{DRIONS-DB} & [PERSON] et al. [21] & 0.8473 & 0.9137 & - & - \\  & [PERSON] et al. [22] & 0.8510 & 0.9102 & - & - \\  & [PERSON] et al. [23] & 0.8862 & 0.9378 & - & - \\  & [PERSON] et al. [8] & 0.8900 & 0.9400 & - & - \\  & [PERSON] et al. [24] & 0.9363 & 0.9679 & - & - \\  & [PERSON] et al. [25] & 0.9474 & 0.9768 & - & - \\  & Proposed & **0.9758** & **0.9877** & & - \\ \hline \multirow{8}{*}{Drishti-GS} & [PERSON] et al. [26] & 0.944 & 0.971 & - & 0.876 \\  & [PERSON] et al. [8] & 0.9444 & 0.9739 & 0.8050 & 0.8910 \\  & [PERSON] et al. [10] & 0.9501 & 0.9743 & 0.8334 & 0.9083 \\  & [PERSON] et al. [11] & 0.9506 & 0.9746 & 0.8213 & 0.8992 \\  & [PERSON] et al. [24] & 0.9531 & 0.9768 & 0.8538 & **0.9195** \\  & [PERSON] et al. [27] & 0.950 & 0.974 & 0.845 & 0.916 \\  & Proposed & **0.9756** & **0.9783** & **0.8405** & 0.9127 \\ \hline \multirow{8}{*}{REFUGE} & Mammoth [28] & - & 0.9361 & - & 0.8667 \\  & SDSAIRC [28] & - & 0.9436 & - & 0.8315 \\  & NKSG [29] & - & 0.9488 & - & 0.8643 \\  & VRT [28] & - & 0.9532 & - & 0.8600 \\  & CUHKMED [28] & - & 0.9602 & - & 0.8826 \\  & [PERSON] et al. [30] & 0.915 & 0.955 & 0.802 & 0.887 \\  & [PERSON] et al. [31] & - & 0.9504 & - & 0.8546 \\  & [PERSON] et al. [24] & - & 0.9693 & - & **0.9082** \\  & [PERSON] et al. [27] & 0.925 & 0.961 & 0.808 & 0.894 \\  & [PERSON] et al. [29] & - & 0.9601 & - & 0.8903 \\  & Proposed & **0.9786** & **0.9892** & **0.8798** & 0.9014 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The segmentation results of OD and OC obtained by the proposed approach and compared approaches. In the Drishti-GS dataset, the proposed model achieved and IoU of 0.9756 and Dice score of 0.9783 for the OD and an IoU of 0.8405 and Dice score of 0.9127 for the OC, showing comparable performance to the current best-performing method by [27] (OD: IoU of 0.950, Dice score of 0.9763; OC: IoU of 0.845, Dice score of 0.9196). While the OD segmentation performance was on par with the SOTA models, the OC Dice score of 0.9127 achieved by our model is noteworthy, given that the OC is structurally smaller, more variable in shape, and often exhibits unclear boundaries. These results underscore the model's strength in learning the spatial containment relationship between the OD and OC through BAT and the added value of Geometry-aware Loss in capturing fine-grained boundary details. On the REFUGE dataset, the proposed model attained an IoU of 0.9786 and Dice score of 0.9892 for the OD and an IoU of 0.8798 and Dice score of 0.9014 for the OC, outperforming both [24] (OD Dice score of 0.9693, OC Dice score of 0.9082) and [31] (OD Dice score of 0.9504, OC Dice score of 0.8546). Notably, the proposed model improved the OC Dice score by 4.68 percentage points compared to [31] and the OD Dice score by 1.99 points compared to [24].