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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Automated Cell Tracking Using Motion Prediction-Based Matching and Event Handling
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Automated Cell Tracking Using Motion Prediction-Based Matching and Event Handling

机译：使用基于运动预测的匹配和事件处理自动单元跟踪

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摘要

Automated cell segmentation and tracking enables the quantification of static and dynamic cell characteristics and is significant for disease diagnosis, treatment, drug development, and other biomedical applications. This paper introduces a method for fully automated cell tracking, lineage construction, and quantification. Cell detection is performed in the joint spatio-temporal domain by a motion diffusion-based Partial Differential Equation (PDE) combined with energy minimizing active contours. In the tracking stage, we adopt a variational joint local-global optical flow technique to determine the motion vector field. We utilize the predicted cell motion jointly with spatial cell features to define a maximum likelihood criterion to find inter-frame cell correspondences assuming Markov dependency. We formulate cell tracking and cell event detection as a graph partitioning problem. We propose a solution obtained by minimization of a global cost function defined over the set of all cell tracks. We construct a cell lineage tree that represents the cell tracks and cell events. Finally, we compute morphological, motility, and diffusivity measures and validate cell tracking against manually generated reference standards. The automated tracking method applied to reference segmentation maps produces an average tracking accuracy score(TRA) of 99 percent, and the fully automated segmentation and tracking system produces an average TRA of 89 percent.
机译：自动细胞分割和跟踪能够定量静态和动态细胞特征，对疾病诊断，治疗，药物开发和其他生物医学应用具有重要意义。本文介绍了一种用于全自动单元跟踪，谱系结构和量化的方法。通过基于运动扩散的部分微分方程（PDE）在关节时空域中在关节时空域中进行细胞检测，与能量最小化有源轮廓。在跟踪阶段，我们采用变分关合本地 - 全局光学流技术来确定运动矢量字段。我们利用具有空间小区特征的预测的小区运动，以定义最大似然标准以找到假设马尔可夫依赖关系的帧间小区对应关系。我们将小区跟踪和单元事件检测制定为图形分区问题。我们提出通过最小化在所有细胞轨道上定义的全球成本函数来获得的解决方案。我们构建一个单元格划分树，表示小区轨道和小区事件。最后，我们计算形态，动力和扩散测量，并验证手动生成的参考标准的单元格跟踪。应用于参考分割图的自动跟踪方法产生99％的平均跟踪精度分数（TRA），并且全自动分割和跟踪系统产生89％的平均TRA。

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