WaveRNet: Wavelet-Guided Frequency Learning for Multi-Source Domain-Generalized Retinal Vessel Segmentation

2026-01-09

arXiv.org

AI Summary

This work addresses the challenges of domain shift caused by uneven illumination and contrast variations in retinal vessel segmentation, as well as the difficulty of existing methods in preserving fine vascular structures. To tackle these issues, the authors propose WaveRNet, a novel framework that enhances the domain generalization capability of the Segment Anything Model (SAM) through wavelet-guided frequency-domain learning. WaveRNet introduces a Spectral Domain Modulation (SDM) mechanism by innovatively integrating wavelet decomposition with learnable domain tokens. Additionally, it incorporates a test-time Frequency-domain Similarity-driven Adaptive Fusion (FADF) module and a Hierarchical Mask Prompt Refiner (HMPR) to recover fine-grained vessel details. Evaluated under the Leave-One-Domain-Out protocol across four public datasets, WaveRNet achieves state-of-the-art domain generalization performance, significantly outperforming existing approaches.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Domain-generalized retinal vessel segmentation is critical for automated ophthalmic diagnosis, yet faces significant challenges from domain shift induced by non-uniform illumination and varying contrast, compounded by the difficulty of preserving fine vessel structures. While the Segment Anything Model (SAM) exhibits remarkable zero-shot capabilities, existing SAM-based methods rely on simple adapter fine-tuning while overlooking frequency-domain information that encodes domain-invariant features, resulting in degraded generalization under illumination and contrast variations. Furthermore, SAM's direct upsampling inevitably loses fine vessel details. To address these limitations, we propose WaveRNet, a wavelet-guided frequency learning framework for robust multi-source domain-generalized retinal vessel segmentation. Specifically, we devise a Spectral-guided Domain Modulator (SDM) that integrates wavelet decomposition with learnable domain tokens, enabling the separation of illumination-robust low-frequency structures from high-frequency vessel boundaries while facilitating domain-specific feature generation. Furthermore, we introduce a Frequency-Adaptive Domain Fusion (FADF) module that performs intelligent test-time domain selection through wavelet-based frequency similarity and soft-weighted fusion. Finally, we present a Hierarchical Mask-Prompt Refiner (HMPR) that overcomes SAM's upsampling limitation through coarse-to-fine refinement with long-range dependency modeling. Extensive experiments under the Leave-One-Domain-Out protocol on four public retinal datasets demonstrate that WaveRNet achieves state-of-the-art generalization performance. The source code is available at https://github.com/Chanchan-Wang/WaveRNet.

Problem

• domain generalization
• retinal vessel segmentation
• domain shift
• fine vessel structures
• illumination variation

Innovation

• Wavelet Decomposition
• Domain Generalization
• Frequency Learning
• Retinal Vessel Segmentation
• Segment Anything Model