VEMamba：基于轴向-横向一致 Mamba 的体电子显微镜高效各向同性重建
VEMamba: Efficient Isotropic Reconstruction of Volume Electron Microscopy with Axial-Lateral Consistent Mamba
将 Mamba 的选择性扫描按轴向-横向分块重排，把各向异性体电镜数据的 3D 依赖压成 1D 序列建模，这套 ALCSSM+DWAM 的重排聚合思路可直接迁移到其他轴向分辨率不足的 3D 医学/生物成像超分与去噪任务。
原文依据 an Axial-Lateral Chunking Selective Scan Module (ALCSSM), which intelligently re-maps complex 3D spatial dependencies (both axial and lateral) into optimized 1D sequences
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论体电子显微镜（VEM）对三维组织成像至关重要，但常产生轴向分辨率较差的各向异性数据，阻碍可视化与下游分析。现有的各向同性重建方法往往忽视丰富的轴向信息，并采用简单下采样来模拟各向异性数据。针对这些局限，我们提出 VEMamba，一个高效的各向同性重建框架。VEMamba 的核心是一种新颖的三维依赖重排序范式，由两个关键组件实现：轴向-横向分块选择性扫描模块（ALCSSM），它将复杂的三维空间依赖（轴向与横向）智能地重映射为优化的一维序列，以便进行高效的 Mamba 建模，并显式地强制轴向-横向一致性；以及动态权重聚合模块（DWAM），用于自适应地聚合这些重排序后的序列输出，以增强表征能力。此外，我们引入一种逼真的退化模拟，并利用动量对比（MoCo）将这种退化感知知识整合进网络，从而获得更优的重建效果。在模拟与真实各向异性 VEM 数据集上的大量实验表明，VEMamba 在多项指标上取得极具竞争力的性能，同时保持较低的计算开销。
英文原文摘要 1361 字符
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.