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arXiv Computer Vision By Bo Zhang, Yifan Zhang, Shuo Yan, Yu Bai, Zheng Zhang, Wu Liu, Xiuzhuang Zhou, Wendong Wang

SSFMamba: Learning Symmetry-driven Spatial-Frequency Modeling for Physically Consistent 3D Medical Image Segmentation

Read the original on arXiv Computer Vision →

SSFMamba is a Mamba-based framework that fuses spatial and frequency-domain modeling for 3D medical image segmentation. It uses a dual-branch design, with a spatial branch preserving voxel-level textures and a frequency branch capturing global context through spectral components. The core 3D Multi-Directional Scanning Mechanism incorporates Hermitian symmetry and State Space Models to enable direction-aware global modeling, leading to superior performance on MRI and CT datasets, especially for low-contrast organs like the pancreas.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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