Beyond Domain Alignment: Anatomically Constrained Unsupervised Adaptation for Optic Nerve Head Segmentation

Glaucoma causes irreversible vision loss, and accurate optic disc (OD) and optic cup (OC) segmentation from fundus images is essential for computing the vertical cup-to-disc ratio (vCDR) used in primary screening. Deep learning has automated OD/OC segmentation, but supervised models degrade under domain shift caused by heterogeneous devices, protocols, and populations. Existing unsupervised domain adaptation (UDA) methods align feature or output distributions but neglect OC-OD anatomical relationship, leading to clinically implausible predictions. We propose an anatomically constrained UDA framework for optic nerve head (ONH) segmentation that jointly addresses feature-level domain discrepancy and anatomical-level population variability. The framework combines automated ROI extraction, a segmentation backbone, and decoder adversarial alignment. To preserve valid ONH geometry without target annotations, a differentiable cup-to-disc area ratio is regularized by a two-sided extended log-barrier, with per-sample bounds adapted via class-conditional mechanism driven by a lightweight classifier. Ablation shows that anatomical regularization benefits only when the source-target cup-to-disc distributions differ significantly. We therefore enable it selectively using a two-sample hypothesis test on per-image ratios, evaluated without target labels. Trained on REFUGE as source domain, the gated framework achieves competitive cross-domain performance, with OC $Dice$ of 0.895 and 0.823 and OD $Dice$ of 0.982 and 0.974 on Drishti-GS and on RIM-ONE-r3, respectively.