用新型序列重排方法提升电子显微镜三维图像各向同性重建质量。
VEMamba: Efficient Isotropic Reconstruction of Volume Electron Microscopy with Axial-Lateral Consistent Mamba
- 通过轴向-侧向分块扫描模块，将3D依赖关系转化为高效1D序列建模。
- 在真实与模拟数据上均实现优异重建效果，计算开销更低。
- 适合需要高精度三维生物成像的科研人员使用。
体积电子显微镜（VEM）对三维组织成像至关重要，但常产生各向异性数据，尤其轴向分辨率差，影响可视化与后续分析。现有各向同性重建方法往往忽略丰富的轴向信息，且采用简单下采样模拟各向异性数据。为此，我们提出VEMamba，一种高效的各向同性重建框架。其核心为新型3D依赖重排范式，包含两个关键组件：轴向-侧向分块选择性扫描模块（ALCSSM），可智能将复杂的3D空间依赖关系（轴向与侧向）重构为优化的1D序列，以实现高效的Mamba建模并明确保证轴向-侧向一致性；动态权重聚合模块（DWAM），用于自适应融合重排后的序列输出，增强表征能力。此外，我们引入真实退化模拟，并利用动量对比（MoCo）将此退化感知知识融入网络，提升重建性能。在多种模拟与真实各向异性VEM数据集上的大量实验表明，VEMamba在多个指标上表现优异，同时保持更低计算开销。源代码已开源于GitHub：https://github.com/I2-Multimedia-Lab/VEMamba。
原文摘要 · Abstract (English)
Volume Electron Microscopy (VEM) is crucial for 3D tissue imaging but often produces anisotropic data with poor axial resolution, hindering visualization and downstream analysis. Existing methods for isotropic reconstruction often suffer from neglecting abundant axial information and employing simple downsampling to simulate anisotropic data. To address these limitations, we propose VEMamba, an efficient framework for isotropic reconstruction. The core of VEMamba is a novel 3D Dependency Reordering paradigm, implemented via two key components: an Axial-Lateral Chunking Selective Scan Module (ALCSSM), which intelligently re-maps complex 3D spatial dependencies (both axial and lateral) into optimized 1D sequences for efficient Mamba-based modeling, explicitly enforcing axial-lateral consistency; and a Dynamic Weights Aggregation Module (DWAM) to adaptively aggregate these reordered sequence outputs for enhanced representational power. Furthermore, we introduce a realistic degradation simulation and then leverage Momentum Contrast (MoCo) to integrate this degradation-aware knowledge into the network for superior reconstruction. Extensive experiments on both simulated and real-world anisotropic VEM datasets demonstrate that VEMamba achieves highly competitive performance across various metrics while maintaining a lower computational footprint. The source code is available on GitHub: https://github.com/I2-Multimedia-Lab/VEMamba
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