SpectralMamba-UNet: Frequency-Disentangled State Space Modeling for Texture-Structure Consistent Medical Image Segmentation

• URL: http://arxiv.org/abs/2602.23103v1
• Date: Thu, 26 Feb 2026 15:17:42 GMT
• Title: SpectralMamba-UNet: Frequency-Disentangled State Space Modeling for Texture-Structure Consistent Medical Image Segmentation
• Authors: Fuhao Zhang, Lei Liu, Jialin Zhang, Ya-Nan Zhang, Nan Mu,
• Abstract summary: We propose SpectralMamba-UNet to decouple the learning of structural and textural information in the spectral domain.<n> Experiments on five public benchmarks demonstrate consistent improvements across diverse modalities and segmentation targets.
• Score: 14.42559964239819
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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