通过小波频域引导，提升多源眼底血管分割在光照变化下的泛化能力。
WaveRNet: Wavelet-Guided Frequency Learning for Multi-Source Domain-Generalized Retinal Vessel Segmentation
- 用小波分解分离光照不变的低频结构与高频血管边界
- 测试时根据频域相似性智能选择域并加权融合，提升适应性
- 分层细化提示，克服SAM上采样丢失细节的问题，适合医学图像分析
领域泛化的眼底血管分割对自动化眼科诊断至关重要，但受非均匀光照和对比度差异引起的域偏移影响严重，且难以保持细小血管结构。尽管分割一切模型（SAM）具备出色的零样本能力，现有基于SAM的方法仅采用简单适配器微调，忽视了编码域不变特征的频域信息，导致在光照与对比度变化下性能下降。此外，SAM的直接上采样不可避免地丢失细小血管细节。为此，我们提出WaveRNet，一种用于鲁棒多源域泛化眼底血管分割的小波引导频域学习框架。具体而言，设计频域引导的域调制器（SDM），结合小波分解与可学习域标记，实现光照鲁棒的低频结构与高频血管边界的分离，并促进域特定特征生成。进一步引入频域自适应域融合（FADF）模块，通过小波基频域相似性进行测试时域选择与软加权融合。最后提出分层掩码提示精修器（HMPR），通过粗到精的细化与长程依赖建模，克服SAM上采样缺陷。在四个公开眼底数据集上采用留一域排除协议的大量实验表明，WaveRNet实现了最先进的泛化性能。代码已开源：https://github.com/Chanchan-Wang/WaveRNet。
原文摘要 · Abstract (English)
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.
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