ArticleFrontiers in neuroscience2026
MambaSSM: efficient segmentation of brain structures in anisotropic 3D EM images via state-space models.
Article in Frontiers in neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurate segmentation of brain structures from anisotropic 3D electron microscopy (EM) images remains challenging due to the trade-off between global context modeling and computational efficiency. While state-space models (SSMs) like Mamba have shown promise in capturing long-range dependencies, their direct application to anisotropic EM data has been limited. We introduce MambaSSM, a novel network that adapts SSMs to anisotropic 3D EM images via a tailored scanning strategy. Our method features two core modules: an SSM-based anisotropic adaptation module for early-stage feature learning and an SSM-based isotropic adaptation module for later-stage refinement. These modules are interleaved with convolutional layers to enable multi-scale feature extraction. Evaluated on two public datasets (SNEMI3D and MitoEM-R), MambaSSM achieves superior segmentation accuracy with significantly lower memory usage compared to CNN, Transformer, and Mamba based baselines.
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