Neuron segmentation is the cornerstone of reconstructing comprehensive neuronal connectomes, which is essential for deciphering the functional organization of the brain. The irregular morphology and densely intertwined structures of neurons make this task particularly challenging. Prevailing CNN-based methods often fail to resolve ambiguous boundaries due to the lack of long-range context, whereas Transformer-based methods suffer from boundary imprecision caused by the loss of voxel-level details during patch partitioning. To address these limitations, we propose NeuroMamba, a multi-perspective framework that exploits the linear complexity of Mamba to enable patch-free global modeling and synergizes this with complementary local feature modeling, thereby efficiently capturing long-range dependencies while meticulously preserving fine-grained voxel details. Specifically, we design a channel-gated Boundary Discriminative Feature Extractor (BDFE) to enhance local morphological cues. Complementing this, we introduce the Spatial Continuous Feature Extractor (SCFE), which integrates a resolution-aware scanning mechanism into the Visual Mamba architecture to adaptively model global dependencies across varying data resolutions. Finally, a cross-modulation mechanism synergistically fuses these multi-perspective features. Our method demonstrates state-of-the-art performance across four public EM datasets, validating its exceptional adaptability to both anisotropic and isotropic resolutions. The source code will be made publicly available.

翻译：神经元分割是重建完整神经元连接组的基石，对于解析大脑的功能组织至关重要。神经元不规则的形态和密集交织的结构使得该任务极具挑战性。主流的基于CNN的方法由于缺乏长程上下文，常常难以解决模糊边界问题；而基于Transformer的方法则因在图像块划分过程中丢失体素级细节，导致边界不精确。为解决这些局限性，我们提出了NeuroMamba，一个多视角框架。该框架利用Mamba的线性复杂度实现无需分块的全局建模，并将其与互补的局部特征建模协同结合，从而在精细保留细粒度体素细节的同时，高效捕获长程依赖关系。具体而言，我们设计了一个通道门控的边界判别特征提取器（BDFE）以增强局部形态学线索。作为补充，我们引入了空间连续特征提取器（SCFE），它将分辨率感知的扫描机制集成到视觉Mamba架构中，以自适应地建模不同数据分辨率下的全局依赖关系。最后，通过一个跨调制机制协同融合这些多视角特征。我们的方法在四个公开的EM数据集上展现了最先进的性能，验证了其对各向异性和各向同性分辨率均具有卓越的适应性。源代码将公开提供。