We present a software-assisted workflow for the alignment and matching of filamentous structures across a three-dimensional (3D) stack of serial images. This is achieved by combining automatic methods, visual validation, and interactive correction. After the computation of an initial automatic matching, 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 labour. The software tool was developed in an interdisciplinary collaboration between computer scientists and cell biologists 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 dealing with imperfect data 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 visualisation of the full image stack is hampered by the size of the data (up to 100 GB). Here, we present a specialised, interactive, semi-automatic solution that considers all requirements for large-scale stitching of filamentous structures in serial-section image stacks. To the best of our knowledge, it is the only currently available tool which is able to process data of the type and size presented here. The key to our solution is a careful design of the visualisation and interaction tools for each processing step to guarantee real-time response, and an optimised workflow that efficiently guides the user through datasets. The final solution presented here is the result of an iterative process with tight feedback loops between the involved computer scientists and cell biologists.

中文翻译：

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连续切片电子断层扫描图像堆栈中丝状结构的半自动拼接

我们提出了一个软件辅助的工作流程，用于对齐和匹配 3D (3D) 串行图像堆栈中的丝状结构。这是通过结合自动方法、视觉验证和交互式校正来实现的。在初始自动匹配的计算之后，用户可以通过交互地校正界标或细丝匹配来不断改进结果。在对已检查区域的视觉质量评估的支持下，这允许在质量和体力劳动之间进行权衡。该软件工具是在计算机科学家和细胞生物学家之间的跨学科合作中开发的，旨在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。为了这，每个纺锤体被切割成一系列半厚的物理切片，获得电子断层扫描图像。然后将串行断层图像缝合和非刚性对齐，以允许跨断层图像边界跟踪和连接 MT。在实践中，单独的自动拼接只能提供不完整的解决方案，因为大的物理失真和低信噪比通常会导致实验困难。尽管处理与样品制备和后续数据收集相关的不完善数据，但为了获得纺锤体的 3D 模型，需要半自动验证和校正以消除拼接错误。然而，由于主轴中有大量 MT（最多 30k）以及由此产生的密集空间排列，对每个 MT 的简单检查太费时了。此外，完整图像堆栈的交互式可视化受到数据大小（最多 100 GB）的阻碍。在这里，我们提出了一种专门的、交互式的、半自动的解决方案，该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拼接的所有要求。据我们所知，它是目前唯一能够处理此处介绍的类型和大小数据的工具。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，以保证实时响应，以及优化的工作流程，有效地引导用户浏览数据集。此处提出的最终解决方案是所涉及的计算机科学家和细胞生物学家之间具有紧密反馈循环的迭代过程的结果。