Dual Gradient Alignment for Unsupervised Domain Adaptation on Optic Disc and Cup Segmentation

Accurate segmentation of the optic disc and cup (OD/OC) in fundus images is crucial for the glaucoma diagnosis and treatment. However, the distribution discrepancies (domain shift) between source domain and target domain hinder the generalization of segmentation models in clinical applications. In this paper, a dual gradient alignment framework (DGDA) for unsupervised domain adaptation is proposed to achieve accurate OD/OC segmentation. Specifically, feature gradient alignment module is well-designed to learn the domain-invariant representation by aligning the feature gradients between source and target domains. Furthermore, task gradient alignment module introduces the meta-learning to learn the task-agnostic representation to simultaneously balance domain adaptation and OD/OC segmentation. Here, meta-learning imitates multi-task training by aligning the gradients between the segmentation and domain classification task. Extensive experiments are conducted on the two public fundus image datasets (Drishti-GS and RIM-ONE-r3) to evaluate the effectiveness of the proposed DGDA. Experimental results demonstrate the DGDA successfully achieves the unsupervised domain adaptive OD/OC segmentation with competitive performance compared with the state-of-the-art.