Optic disc and cup segmentation using Vision Mamba UNet with enhanced edge-guided attention in fundus images

Background Glaucoma is an irreversible blinding disease for which early detection is essential. Precise segmentation of the optic disc (OD) and optic cup (OC) enables accurate cup-to-disc ratio (CDR) calculation, which serves as an important reference for glaucoma evaluation. However, the ambiguous boundaries between these structures make manual segmentation time-consuming and highly subjective. Methods This study proposes a hybrid framework for OD and OC segmentation in fundus images. The framework first adopts YOLOv10 to achieve coarse localization and extraction of the OD region, followed by fine-grained segmentation using the proposed EEGA-VMUNet network. Based on a Vision Mamba-UNet backbone, we design an enhanced edge-guided attention (EEGA) module to adaptively integrate multi-scale contextual information. This module strengthens the representation of edges and fine structural features. A multi-scale loss function is also used to improve robustness to scale variations. Results Experiments on four benchmark datasets demonstrate that EEGA-VMUNet achieves competitive segmentation performance. On the same-domain test sets, OD Dice coefficients range from 95.20% to 97.65% and OC Dice coefficients range from 87.51% to 89.95%. Zero-shot cross-domain experiments achieve an OD Dice of 95.18% and an OC Dice of 85.13%. Conclusion The proposed model achieves competitive performance in both OD and OC segmentation tasks. Bland-Altman analysis suggests that the derived vertical cup-to-disc ratio (VCDR) holds potential as a diagnostic aid for glaucoma screening, although its precision may require further improvement for longitudinal monitoring.