Clinically Interpretable Geometric Constraints for Optic Cup and Disc Segmentation

We present GCN-CIS, a segmentation framework that embeds clinically interpretable geometric constraints into deep networks for optic cup and disc delineation in fundus images. A Prior Boundary Attention Module (PBAM) sharpens boundary predictions, while an ISNT-guided geometric loss enforces anatomically plausible regional ordering. Evaluated on an internal glaucoma screening set and three public benchmarks (REFUGE, ORIGA, Drishti-GS), GCN-CIS yields consistent gains in cup/disc Dice and reduces vCDR error compared to strong baselines. The method improves clinical reliability of segmentation outputs and facilitates efficient vCDR estimation for screening workflows. Code will be released.