Volume electron microscopy is the method of choice for the in-situ interrogation of cellular ultrastructure at the nanometer scale. Recent technical advances have led to a rapid increase in large raw image datasets that require computational strategies for segmentation and spatial analysis. In this protocol, we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis, and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation. We specifically target researchers in the life sciences with limited computational expertise, who face the following tasks within their volume electron microscopy projects: i) How to generate 3D segmentation labels for different types of cell organelles while minimizing manual annotation efforts, ii) how to analyze the spatial interactions between organelle instances, and iii) how to best visualize the 3D segmentation results. To meet these demands we give detailed guidelines for choosing the most efficient segmentation tools for the specific cell organelle. We furthermore provide easily executable components for spatial analysis and 3D rendering and bridge compatibility issues between freely available open-source tools, such that others can replicate our full pipeline starting from a raw dataset up to the final plots and rendered images. We believe that our detailed description can serve as a valuable reference for similar projects requiring special strategies for single- or multiple organelle analysis which can be achieved with computational resources commonly available to single-user setups.

翻译：体积电子显微镜是用于在纳米尺度原位探查细胞超微结构的首选方法。近期技术进展导致原始图像数据集迅速增长，亟需计算策略进行分割与空间分析。本方案中，我们描述了一条实用且标注高效的工作流程，用于对大规模体积电子显微镜数据集进行细胞器特异性分割、空间分析与可视化，该流程基于免费、用户友好的软件工具，可在单台标准工作站上运行。我们特别针对计算专业知识有限的生命科学研究者，他们在体积电子显微镜项目中面临以下任务：i) 如何在最小化人工标注工作量的情况下生成不同细胞器类型的3D分割标签；ii) 如何分析细胞器实例之间的空间相互作用；iii) 如何最优地可视化3D分割结果。为满足这些需求，我们提供了详细指南，以选择针对特定细胞器最高效的分割工具。此外，我们还提供了易于执行的空间分析与3D渲染组件，并解决了免费开源工具之间的兼容性问题，使得他人可从原始数据集开始，完整复现我们的全流程直至最终图表与渲染图像。我们相信，这份详细描述可为类似项目提供重要参考，特别是那些需采用特殊策略进行单细胞器或多细胞器分析、且仅需单用户常用计算资源的项目。