Decoupling Wavelet Sub-bands for Single Source Domain Generalization in Fundus Image Segmentation

TL;DR

WaveSDG is a wavelet-guided segmentation network that decouples anatomical structure from domain-specific appearance, improving single-source domain generalization in fundus image segmentation across unseen datasets.

Contribution

The paper introduces WaveSDG with the WISER module, a novel wavelet-based approach that enhances domain generalization by decoupling anatomy from appearance in fundus images.

Findings

WaveSDG outperforms seven state-of-the-art methods on multiple unseen datasets.

The WISER module effectively refines features by leveraging wavelet sub-bands.

WaveSDG achieves better accuracy and robustness with lower variance across datasets.

Abstract

Domain generalization in fundus imaging is challenging due to variations in acquisition conditions across devices and clinical settings. The inability to adapt to these variations causes performance degradation on unseen domains for deep learning models. Besides, obtaining annotated data across domains is often expensive and privacy constraints restricts their availability. Although single-source domain generalization (SDG) offers a realistic solution to this problem, the existing approaches frequently fail to capture anatomical topology or decouple appearance from anatomical features. This research introduces WaveSDG, a new wavelet-guided segmentation network for SDG. It decouples anatomical structure from domain-specific appearance through a wavelet sub-band decomposition. A novel Wavelet-based Invariant Structure Extraction and Refinement (WISER) module is proposed to process encoder features by leveraging distinct semantic roles of each wavelet sub-band. The module refines low-frequency components to anchor global anatomy, while selectively enhancing directional edges and suppressing noise within the high-frequency sub-bands. Extensive ablation studies validate the effectiveness of the WISER module and its decoupling strategy. Our evaluations on optic cup and optic disc segmentation across one source and five unseen target datasets show that WaveSDG consistently outperforms seven state-of-the-art methods. Notably, it achieves the best balanced Dice score and lowest 95th percentile Hausdorff distance with reduced variance, indicating improved accuracy, robustness, and cross-domain stability.