# Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images
## 8 Conclusions  
This study proposed a novel segmentation network that integrates structure-preserving data augmentation, BAT, and Geometry-aware Loss to achieve more precise delineation of boundaries between the OD and OC in fundus images. Through truncated Gaussian sampling and various colormap transformations, we enhanced vascular and tissue structures while introducing only meaningful morphological variations, thereby simultaneously achieving data diversity and structural preservation. The BAT module was designed to explicitly learn the structural inclusion relationship between the OD and OC usingmulti-resolution feature maps, effectively improving boundary recognition performance. Additionally, to address the limitations of conventional Dice Loss and to quantitatively reduce errors near boundaries, we introduced a Geometry-aware Loss based on a normalized Hausdorff Distance.  
Across multiple publicly available datasets, the proposed model outperformed existing state-of-the-art segmentation models, with particularly notable improvements in the OC regions where boundary recognition is critical. Ablation studies and visual analyses demonstrated that each component independently contributed to meaningful performance improvements, and when combined, the three components exhibited a complementary synergistic effect, achieving the highest levels of precision and boundary consistency.  
In conclusion, this study presents an integrated approach capable of achieving both precise boundary recognition and structural preservation, which are essential requirements for medical image segmentation. The proposed method shows strong potential for future expansion into glaucoma diagnosis, diagnostic assistance systems, and real-world clinical applications, and it is expected to be applicable to various medical image analysis tasks where precise boundary segmentation is critical.  
Conceptualization, S.W. and D.-S.E.; methodology, S.W. and D.-S.E.; software, S.W.; validation, B.K.; formal analysis, S.W.; investigation, B.K.; resources, S.W.; data curation, B.K.; writing--original draft preparation, S.W.; writing--review and editing, B.K. and D.-S.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors would like to thank the members of the Future Information Network Architecture Laboratory in Korea University.  
Author [PERSON] was employed by the company Sensorway. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} OD & Optic Disc \\ OC & Optic Cup \\ CDR & Cup-to-Disc Ratio \\ ROI & Region of Interest \\ BAT & Boundary-aware Transformer Attention \\ ViT & Vision Transformer \\ CCNet & Criss-Cross Network \\ OCT & Optical Coherence Tomography \\ CNN & Convolutional Neural Network \\ IoU & Intersection over Union \\ mIoU & Mean Intersection over Union \\ GT & Ground Truth \\ FFN & Feedforward Network \\ MHSA & Multi-Head Self-Attention \\ HD & Hausdorff Distance \\ \end{tabular}