发表机构
Chinese Institute for Brain Research; Academy for Advanced Interdisciplinary Studies, Peking University（北京脑科学与类脑研究所; 北京大学前沿交叉学科研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SkNeXt提出拓扑优先框架，通过SWC骨架引导重建并作为空间索引选择性访问数据，在单GPU一周内完成小鼠大脑拍字节级数据的神经元重建，大幅降低计算与I/O开销。
AI 中文摘要
高分辨率荧光和电子显微镜的最新进展使得纳米级成像能够覆盖日益增大的脑体积，但由此产生的太字节级至拍字节级数据集使得完整的神经元重建在计算、数据移动和人工校对方面代价高昂。在此，我们提出SkNeXt，一个用于从大规模体积显微镜数据集进行可扩展神经元重建的拓扑优先框架。SkNeXt并非密集处理整个图像体积，而是首先将神经元形态转换为保留长程连接性的紧凑SWC骨架。因此，校对工作聚焦于稀疏的神经元树，使得分支、连续性和连接性错误能够在高分辨率重建之前得到纠正。随后，纠正后的骨架作为持久的结构先验，用于恢复详细形态，同时保留神经元身份和拓扑。关键在于，SkNeXt还利用神经元骨架作为空间索引以实现选择性数据访问，仅沿重建轨迹检索高分辨率图像区域，从而绕过大部分背景和无信号体积。这大幅减少了I/O和计算开销，使重建成本随神经元形态而非总数据集大小扩展。利用SkNeXt，我们在单块GPU上于一周内从小鼠大脑的拍字节级超分辨率荧光数据集中重建了神经元，而无需对整个成像体积进行穷举式密集推理。
英文摘要
Recent advances in high-resolution fluorescence and electron microscopy have enabled nanoscale imaging across increasingly large brain volumes, but the resulting terabyte- to petabyte-scale datasets make complete neuronal reconstruction prohibitively expensive in computation, data movement, and manual proofreading. Here, we present SkNeXt, a topology-first framework for scalable neuronal reconstruction from large volumetric microscopy datasets. Instead of densely processing entire image volumes, SkNeXt first converts neuronal morphology into compact SWC skeletons that preserve long-range connectivity. Proofreading is therefore focused on sparse neuronal trees, allowing branch, continuity, and connectivity errors to be corrected before high-resolution reconstruction. The corrected skeletons then serve as persistent structural priors for recovering detailed morphology while preserving neuronal identity and topology. Crucially, SkNeXt also uses neuronal skeletons as spatial indices for selective data access, retrieving high-resolution image regions only along reconstructed trajectories and bypassing most background and signal-free volumes. This substantially reduces I/O and computational overhead, allowing reconstruction cost to scale with neuronal morphology rather than total dataset size. Using SkNeXt, we reconstructed neurons from a petabyte-scale super-resolution fluorescence dataset of the mouse brain on a single GPU within one week, without requiring exhaustive dense inference across the complete imaging volume.
