In biomedical research, the migration behavior of cells and interactions between various cell types are frequently studied subjects. An automated and quantitative analysis of time-lapse microscopy data is an essential component of these studies, especially when characteristic migration patterns need to be identified. Plenty of software tools have been developed to serve this need. However, the majority of algorithms is designed for fluorescently labeled cells, even though it is well-known that fluorescent labels can substantially interfere with the physiological behavior of interacting cells. We here present a fully revised version of our algorithm for migration and interaction tracking (AMIT), which includes a novel segmentation approach. This approach allows segmenting label-free cells with high accuracy and also enables detecting almost all cells within the field of view. With regard to cell tracking, we designed and implemented a new method for cluster detection and splitting. This method does not rely on any geometrical characteristics of individual objects inside a cluster but relies on monitoring the events of cell–cell fusion from and cluster fission into single cells forward and backward in time. We demonstrate that focusing on these events provides accurate splitting of transient clusters. Furthermore, the substantially improved quantitative analysis of cell migration by the revised version of AMIT is more than two orders of magnitude faster than the previous implementation, which makes it feasible to process video data at higher spatial and temporal resolutions.

中文翻译：

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增强的无标记细胞分割，用于自动迁移和交互跟踪

在生物医学研究中，细胞的迁移行为和各种细胞类型之间的相互作用是经常被研究的课题。延时显微镜数据的自动化和定量分析是这些研究的重要组成部分，尤其是在需要识别特征迁移模式时。已经开发了许多软件工具来满足这种需求。然而，大多数算法都是为荧光标记的细胞设计的，尽管众所周知荧光标记会严重干扰相互作用细胞的生理行为。我们在这里展示了我们的迁移和交互跟踪 (AMIT) 算法的完全修订版本，其中包括一种新颖的分割方法。这种方法允许以高精度分割无标记细胞，并且还能够检测视野内的几乎所有细胞。关于细胞跟踪，我们设计并实现了一种新的聚类检测和分裂方法。该方法不依赖于集群内单个对象的任何几何特征，而是依赖于实时监控细胞-细胞融合和集群分裂成单个细胞的事件。我们证明，关注这些事件可以准确拆分瞬态集群。此外，AMIT 修订版对细胞迁移的定量分析比以前的实现快了两个数量级以上，这使得以更高的空间和时间分辨率处理视频数据成为可能。