# Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images
## 5 Results and Analysis
### Performance Analysis on Different Datasets
clearly indicate that the model successfully focuses on the boundary areas, confirming that the BAT attention mechanism contributed significantly to the improved performance. The ORIGA dataset reflects a wide range of optical conditions and imaging techniques, which introduces additional challenges for segmentation models. In particular, several images in the dataset exhibit ambiguous boundaries between the OD and OC, leading many existing models to produce inaccurate or inconsistent boundary predictions. Despite these difficulties, the proposed model achieved an IoU of 0.976 and a Dice score of 0.9879, representing the highest performance on this dataset. Compared to the previous best-performing model, Swin-UNet (IoU of 0.960, Dice score of 0.9719), our  
Figure 13: Segmentation results on fundus images from the ORIGA dataset. (**a**) input fundus images, (**b**) ground truth, (**c**) predictions mask, and (**d**) predictions from the proposed model with attention maps. approach shows a 1.6% improvement in the Dice score, demonstrating its superior ability to handle complex and low-contrast boundary regions. As shown in Figures 13 and 14, the proposed model performed more precise boundary segmentation than existing models in both full fundus images and ROI views. In particular, the model more accurately reflects the relative size and spatial alignment of the OD and OC and produces smooth, consistent boundary predictions, especially in ROI regions where fine structural detail is critical for clinical interpretation. Figure 14: Segmentation results on ROI images from the ORIGA dataset. (**a**) ROI images, (**b**) ROI ground truth, (**c**) predictions mask, and (**d**) predictions from the proposed model with attention maps. The G1020 dataset contains fundus images of varying quality, making it well-suited for evaluating the robustness of segmentation models. Existing segmentation methods often struggle with low-quality or low-contrast images, leading to inaccurate boundary predictions and performance degradation under such conditions. The proposed model demonstrated strong performance on the G1020 dataset, achieving an IoU of 0.9696 and a Dice score of 0.9783 and significantly outperforming Swin-UNet, which achieved an IoU of 0.9503 and a Dice score of 0.9613. These results indicate that the proposed model is capable of maintaining stable and accurate predictions even across images with varying levels of quality thanks to its enhanced boundary awareness and contextual understanding. Figure 15: Segmentation results on fundus images from the G1020 dataset. (**a**) input fundus images, (**b**) ground truth, (**c**) predictions mask, and (**d**) predictions from the proposed model with attention maps. As illustrated in Figures 15 and 16, the proposed model achieved sharper and more reliable boundary segmentation compared to existing methods, both in full images and ROI views. In particular, for the OC region, which is more prone to boundary ambiguity in low-quality images, the proposed model was able to produce clearer and more accurate delineations, where baseline models tended to generate blurred or incomplete segmentations.