DDS-UDA: Dual-Domain Synergy for Unsupervised Domain Adaptation in Joint Segmentation of Optic Disc and Optic Cup

Abstract

Convolutional neural networks (CNNs) have achieved exciting performance in joint segmentation of optic disc and optic cup on single-institution datasets. However, their clinical translation is hindered by two major challenges: limited availability of large-scale, high-quality annotations and performance degradation caused by domain shift during deployment across heterogeneous imaging protocols and acquisition platforms. While unsupervised domain adaptation (UDA) provides a way to mitigate these limitations, most existing approaches do not address cross-domain interference and intra-domain generalization within a unified framework. In this paper, we present the Dual-Domain Synergy UDA (DDS-UDA), a novel UDA framework that comprises two key modules. First, a bi-directional cross-domain consistency regularization module is enforced to mitigate cross-domain interference through feature-level semantic information exchange guided by a coarse-to-fine dynamic mask generator, suppressing noise propagation while preserving structural coherence. Second, a frequency-driven intra-domain pseudo label learning module is used to enhance intra-domain generalization by synthesizing spectral amplitude-mixed supervision signals, which ensures high-fidelity feature alignment across domains. Implemented within a teacher-student architecture, DDS-UDA disentangles domain-specific biases from domain-invariant feature-level representations, thereby achieving robust adaptation to heterogeneous imaging environments. We conduct a comprehensive evaluation of our proposed method on two multi-domain fundus image datasets, demonstrating that it outperforms several existing UDA based methods and therefore providing an effective way for optic disc and optic cup segmentation.

Figures (7)

• Figure 1: Overview of our proposed DDS-UDA architecture, where we use the teacher-student network as our basic network. Specifically, the bi-directional cross-domain consistency pathway (top) employs a dynamic mask generator (left) that facilitates bi-directional feature-level semantic communication between the source and target domains by generating masks from coarse to fine, which are later trained by adding the same masks at loss computation in a consistency regularization manner. Meanwhile, the intra-domain pseudo label learning pathway (bottom) extracts the average amplitude in the source domain data to synthesize stylized but semantically consistent target domain images, which are fed into the teacher model to generate pseudo labels to supervise the original target domain images.
• Figure 2: The visualization results produced by different methods on the Fundus dataset. Red and blue colors denote OC and OD, respectively.
• Figure 3: Boxplot of the OC/OD segmentation results produced by the different components in DDS-UDA for the experiment on the Fundus dataset "Domain 1".
• Figure 4: Accumulative bar diagram for OC/OD segmentation performance of different intra- and cross-domain data processing techniques on the Fundus dataset.
• Figure 5: The influence of the tunable parameters, i.e., $\lambda_\mathcal{S}$, $\lambda_\mathcal{T}$, and $\lambda_{\mathcal{T}\text{-stylized}}$, on the "Domain 1" scenario of the Fundus dataset.