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.
Radically different