We present a software-assisted workflow for the alignment and matching of filamentous structures across a 3D stack of serial images. This is achieved by combining automatic methods, visual validation, and interactive correction. After an initial alignment, the user can continuously improve the result by interactively correcting landmarks or matches of filaments. Supported by a visual quality assessment of regions that have been already inspected, this allows a trade-off between quality and manual labor. The software tool was developed to investigate cell division by quantitative 3D analysis of microtubules (MTs) in both mitotic and meiotic spindles. For this, each spindle is cut into a series of semi-thick physical sections, of which electron tomograms are acquired. The serial tomograms are then stitched and non-rigidly aligned to allow tracing and connecting of MTs across tomogram boundaries. In practice, automatic stitching alone provides only an incomplete solution, because large physical distortions and a low signal-to-noise ratio often cause experimental difficulties. To derive 3D models of spindles despite the problems related to sample preparation and subsequent data collection, semi-automatic validation and correction is required to remove stitching mistakes. However, due to the large number of MTs in spindles (up to 30k) and their resulting dense spatial arrangement, a naive inspection of each MT is too time consuming. Furthermore, an interactive visualization of the full image stack is hampered by the size of the data (up to 100 GB). Here, we present a specialized, interactive, semi-automatic solution that considers all requirements for large-scale stitching of filamentous structures in serial-section image stacks. The key to our solution is a careful design of the visualization and interaction tools for each processing step to guarantee real-time response, and an optimized workflow that efficiently guides the user through datasets.

中文翻译：

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半自动拼接序列扫描电子断层扫描图像堆栈中的丝状结构

我们提出了一个软件辅助的工作流程，用于在串行图像的3D堆栈中对齐和匹配丝状结构。这是通过结合自动方法，视觉验证和交互式校正来实现的。初始对齐后，用户可以通过交互校正地标或灯丝匹配来不断改善结果。在对已经检查过的区域进行视觉质量评估的支持下，可以在质量和体力劳动之间进行权衡。开发了该软件工具，以通过对有丝分裂和减数分裂纺锤体中的微管（MT）进行定量3D分析来研究细胞分裂。为此，将每个主轴切成一系列半厚的物理部分，并从中获取电子断层图。然后，将连续的断层图缝合并进行非刚性对齐，以允许跨断层图边界跟踪和连接MT。在实践中，仅自动缝合只能提供不完整的解决方案，因为较大的物理失真和较低的信噪比通常会导致实验困难。尽管存在与样品制备和后续数据收集相关的问题，但仍要获得主轴的3D模型，则需要半自动验证和校正以消除针迹错误。但是，由于主轴中的MT数量众多（最多30k），并且它们导致了密集的空间布置，因此对每个MT进行幼稚的检查非常耗时。此外，整个图像堆栈的交互式可视化受到数据大小（最大100 GB）的阻碍。在这里，我们提出了一种专门的，交互式的，一种半自动解决方案，考虑了对连续切片图像堆栈中的丝状结构进行大规模缝合的所有要求。我们解决方案的关键是为每个处理步骤精心设计可视化和交互工具，以确保实时响应，以及优化的工作流程，可有效指导用户浏览数据集。