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.

翻译：域泛化视网膜血管分割对于自动化眼科诊断至关重要，但面临着由非均匀光照和对比度变化引起的域偏移带来的重大挑战，同时还需克服保留细微血管结构的困难。尽管Segment Anything Model (SAM)展现出卓越的零样本能力，但现有基于SAM的方法依赖于简单的适配器微调，忽视了编码域不变特征的频域信息，导致其在光照和对比度变化下的泛化性能下降。此外，SAM的直接上采样操作不可避免地会丢失细微血管细节。为应对这些局限，我们提出了WaveRNet，一种基于小波引导频率学习的鲁棒多源域泛化视网膜血管分割框架。具体而言，我们设计了一种谱引导域调制器(SDM)，它将小波分解与可学习的域令牌相结合，能够分离对光照鲁棒的低频结构和高频血管边界，同时促进域特定特征的生成。此外，我们引入了频率自适应域融合(FADF)模块，该模块通过基于小波的频率相似度计算与软加权融合，实现智能化的测试时域选择。最后，我们提出了分层掩码提示优化器(HMPR)，它通过结合长程依赖建模的由粗到细优化过程，克服了SAM的上采样限制。在四个公开视网膜数据集上采用留一域出协议进行的大量实验表明，WaveRNet实现了最先进的泛化性能。源代码可在https://github.com/Chanchan-Wang/WaveRNet获取。