Unsupervised Domain Adaptation With Anatomical-Aware Self-Training for Optic Disc Segmentation in Abnormal Fundus Images

Optic disc (OD) segmentation in abnormal fundus images is crucial for glaucoma screening, and different screening populations may alter the types and proportions of abnormalities. Since annotating all abnormal types or re-annotating for each screening scenario is costly, an alternative is to utilize existing annotated data. However, these datasets only contain limited abnormal types, leading to a domain shift issue. Unsupervised domain adaptation alleviates this issue through adversarial learning or self-training. Yet, adversarial learning methods tend to overemphasize brightness as a discriminative feature, which fails under pathological changes, while self-training approaches remain vulnerable to noisy pseudo-labels. Existing denoising methods assume noise lies near decision boundaries, but abnormalities can produce noise far from them. In this letter, we propose an unsupervised domain adaptation method integrating anatomical-aware self-training with adversarial learning for OD segmentation. By exploiting the OD's convex shape and boundary consistency, we develop two pseudo-labeling strategies to suppress noise. Experiments on four fundus image datasets demonstrate the effectiveness of our method in diverse screening scenarios.