Cell Tracking Accuracy Measurement Based on Comparison of Acyclic Oriented Graphs.
Cell Tracking Accuracy Measurement Based on Comparison of Acyclic Oriented Graphs.
复制标题
细胞跟踪精度测量基于无环的图表的比较。
DOI：
10.1371/journal.pone.0144959
复制
发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Kozubek M
中科院分区：
文献类型：
--
作者：
Matula P;Maška M;Sorokin DV;Matula P;Ortiz-de-Solórzano C;Kozubek M
在时间推移系列中跟踪运动细胞是具有挑战性的，并且在许多生物医学应用中是必需的。细胞轨迹可以在数学上表示为非循环定向图。它们的顶点描述单个细胞的时空位置，而边缘表示它们之间的时间关系。这样的表示保持了捕获的视场内的所有重要细胞事件的知识，例如迁移、分裂、死亡和通过视场的运输。越来越多的细胞跟踪算法要求比较它们的性能。然而，缺乏标准化的小区跟踪精度测量使得比较不切实际。本文定义和评估的准确性措施，客观和系统的基准细胞跟踪算法。该措施假设存在一个地面实况参考，并评估它是多么困难的计算图转换成参考。难度被测量为使图相同所需的最低数量的图操作的加权和，例如分裂、删除和添加顶点以及删除、添加和改变边的语义。基于2013年IEEE生物医学成像国际研讨会主办的第一届细胞跟踪挑战赛的参与者提供的跟踪结果，对测量行为进行了广泛的分析。我们证明了对不同的细胞跟踪算法和荧光显微镜数据集的权重选择的微小变化的措施的鲁棒性和稳定性。由于该措施惩罚了跟踪结果中所有可能的错误并且易于计算，因此它可以特别帮助开发人员和分析师根据他们的需求调整他们的算法。
Tracking motile cells in time-lapse series is challenging and is required in many biomedical applications. Cell tracks can be mathematically represented as acyclic oriented graphs. Their vertices describe the spatio-temporal locations of individual cells, whereas the edges represent temporal relationships between them. Such a representation maintains the knowledge of all important cellular events within a captured field of view, such as migration, division, death, and transit through the field of view. The increasing number of cell tracking algorithms calls for comparison of their performance. However, the lack of a standardized cell tracking accuracy measure makes the comparison impracticable. This paper defines and evaluates an accuracy measure for objective and systematic benchmarking of cell tracking algorithms. The measure assumes the existence of a ground-truth reference, and assesses how difficult it is to transform a computed graph into the reference one. The difficulty is measured as a weighted sum of the lowest number of graph operations, such as split, delete, and add a vertex and delete, add, and alter the semantics of an edge, needed to make the graphs identical. The measure behavior is extensively analyzed based on the tracking results provided by the participants of the first Cell Tracking Challenge hosted by the 2013 IEEE International Symposium on Biomedical Imaging. We demonstrate the robustness and stability of the measure against small changes in the choice of weights for diverse cell tracking algorithms and fluorescence microscopy datasets. As the measure penalizes all possible errors in the tracking results and is easy to compute, it may especially help developers and analysts to tune their algorithms according to their needs.