IEEE/ACM Trans Comput Biol Bioinform. 2020 May-Jun;17(3):959-971. doi: 10.1109/TCBB.2018.2875684. Epub 2018 Oct 12.
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.
自动细胞分割和跟踪可实现静态和动态细胞特征的量化，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。本文介绍了一种全自动细胞跟踪、谱系构建和量化的方法。通过基于运动扩散的偏微分方程（PDE）与能量最小化主动轮廓相结合，在时空联合域中进行细胞检测。在跟踪阶段，我们采用变分联合局部-全局光流技术来确定运动矢量场。我们利用预测的细胞运动以及空间细胞特征来定义最大似然准则，以在假设 Markov 依赖性的情况下找到帧间细胞对应关系。我们将细胞跟踪和细胞事件检测表述为图划分问题。我们提出了一种通过最小化定义在所有细胞轨迹集合上的全局成本函数来获得的解决方案。我们构建了一个表示细胞轨迹和细胞事件的细胞谱系树。最后，我们计算形态、运动性和扩散性度量，并针对手动生成的参考标准验证细胞跟踪。应用于参考分割图的自动跟踪方法产生了 99%的平均跟踪准确性得分（TRA），而全自动分割和跟踪系统产生了 89%的平均 TRA。
