通过频域解耦建模，提升医学图像分割的结构与纹理一致性
SpectralMamba-UNet: Frequency-Disentangled State Space Modeling for Texture-Structure Consistent Medical Image Segmentation
- 在频域分解高低频特征，分别用Mamba和注意力机制建模
- 在五个公开数据集上均实现更优分割性能，跨模态通用性强
- 适合需要精细边界与整体结构协同建模的医学图像任务
精准的医学图像分割需同时建模全局解剖结构与细微边界特征。现有状态空间模型（如Vision Mamba）虽能高效捕捉长程依赖，但其一维序列化处理削弱了局部空间连续性与高频表征能力。为此，我们提出SpectralMamba-UNet，一种频域解耦框架，通过离散余弦变换分离低频与高频特征：低频用于频域Mamba进行全局上下文建模，高频则保留边界敏感细节。为平衡频域贡献，引入频域通道重加权（SCR）机制生成通道级频率感知注意力，并设计频域引导融合（SGF）模块实现解码器中自适应多尺度融合。在五个公开基准上的实验表明，该方法在多种模态与分割目标下均取得一致改进，验证了其有效性和泛化能力。
原文摘要 · Abstract (English)
Accurate medical image segmentation requires effective modeling of both global anatomical structures and fine-grained boundary details. Recent state space models (e.g., Vision Mamba) offer efficient long-range dependency modeling. However, their one-dimensional serialization weakens local spatial continuity and high-frequency representation. To this end, we propose SpectralMamba-UNet, a novel frequency-disentangled framework to decouple the learning of structural and textural information in the spectral domain. Our Spectral Decomposition and Modeling (SDM) module applies discrete cosine transform to decompose low- and high-frequency features, where low frequency contributes to global contextual modeling via a frequency-domain Mamba and high frequency preserves boundary-sensitive details. To balance spectral contributions, we introduce a Spectral Channel Reweighting (SCR) mechanism to form channel-wise frequency-aware attention, and a Spectral-Guided Fusion (SGF) module to achieve adaptively multi-scale fusion in the decoder. Experiments on five public benchmarks demonstrate consistent improvements across diverse modalities and segmentation targets, validating the effectiveness and generalizability of our approach.
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