NeuroMamba：基于视觉Mamba的多视角特征交互用于神经元分割
NeuroMamba: Multi-Perspective Feature Interaction with Visual Mamba for Neuron Segmentation
AI总结：
NeuroMamba通过多视角特征交互和视觉Mamba架构，提升神经元分割的精度与适应性，适用于不同分辨率的数据。
摘要：
神经元分割是重建全面神经元连接图谱的基石，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紧密交织的结构使这一任务特别具有挑战性。现有的基于CNN的方法由于缺乏长程上下文而常常无法解决模糊边界的问题，而基于Transformer的方法则由于在块划分过程中丢失了体素级细节而导致边界不精确。为了解决这些限制，我们提出了NeuroMamba，一个多视角框架，利用Mamba的线性复杂度实现无块全局建模，并与互补的局部特征建模相结合，从而高效地捕捉长程依赖关系，同时细致地保留细粒度的体素细节。具体来说，我们设计了一个通道门控边界判别特征提取器（BDFE）来增强局部形态线索。此外，我们引入了空间连续特征提取器（SCFE），该提取器将一种分辨率感知的扫描机制整合到视觉Mamba架构中，以适应性地建模不同数据分辨率下的全局依赖关系。最后，一种交叉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数据集上展示了最先进的性能，验证了其卓越的适应性，能够应对各向同性和各向异性分辨率。源代码将被公开发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
