Versari Cristian, Stoma Szymon, Batmanov Kirill, Llamosi Artémis, Mroz Filip, Kaczmarek Adam, Deyell Matt, Lhoussaine Cédric, Hersen Pascal, Batt Gregory
BioComputing, CRIStAL, Université Lille 1, Lille, France
Scientific Center for Optical and Electron Microscopy (ScopeM), ETH Zurich, Zurich, Switzerland.
J R Soc Interface. 2017 Feb;14(127). doi: 10.1098/rsif.2016.0705.
With the continuous expansion of single cell biology, the observation of the behaviour of individual cells over extended durations and with high accuracy has become a problem of central importance. Surprisingly, even for yeast cells that have relatively regular shapes, no solution has been proposed that reaches the high quality required for long-term experiments for segmentation and tracking (S&T) based on brightfield images. Here, we present , a tool chain designed to achieve good performance in long-term experiments. The key features are the use of a new variant of parametrized active rays for segmentation, a neighbourhood-preserving criterion for tracking, and the use of an iterative approach that incrementally improves S&T quality. A graphical user interface enables manual corrections of S&T errors and their use for the automated correction of other, related errors and for parameter learning. We created a benchmark dataset with manually analysed images and compared with six other tools, showing its high performance, notably in long-term tracking. As a community effort, we set up a website, the Yeast Image Toolkit, with the benchmark and the to gather this and additional information provided by others.
随着单细胞生物学的不断扩展，长时间高精度观察单个细胞的行为已成为一个至关重要的问题。令人惊讶的是，即使对于形状相对规则的酵母细胞，也没有提出一种基于明场图像的分割和跟踪（S&T）方法能达到长期实验所需的高质量要求。在这里，我们展示了一个旨在在长期实验中实现良好性能的工具链。其关键特性包括使用一种新的参数化有源射线变体进行分割、一种用于跟踪的邻域保留准则，以及使用一种迭代方法来逐步提高S&T质量。图形用户界面允许手动校正S&T错误，并将其用于自动校正其他相关错误以及参数学习。我们创建了一个带有手动分析图像的基准数据集，并将其与其他六个工具进行比较，展示了其高性能，尤其是在长期跟踪方面。作为一项社区工作，我们建立了一个网站——酵母图像工具包，其中包含基准数据集和该工具链，以收集这些信息以及其他人提供的其他信息。
