Significance: Machine learning is increasingly being applied to the classification of microscopic data. In order to detect some complex and dynamic cellular processes, time-resolved live-cell imaging might be necessary. Incorporating the temporal information into the classification process may allow for a better and more specific classification. Aim: We propose a methodology for cell classification based on the time-lapse quantitative phase images (QPIs) gained by digital holographic microscopy (DHM) with the goal of increasing performance of classification of dynamic cellular processes. Approach: The methodology was demonstrated by studying epithelial–mesenchymal transition (EMT) which entails major and distinct time-dependent morphological changes. The time-lapse QPIs of EMT were obtained over a 48-h period and specific novel features representing the dynamic cell behavior were extracted. The two distinct end-state phenotypes were classified by several supervised machine learning algorithms and the results were compared with the classification performed on single-time-point images. Results: In comparison to the single-time-point approach, our data suggest the incorporation of temporal information into the classification of cell phenotypes during EMT improves performance by nearly 9% in terms of accuracy, and further indicate the potential of DHM to monitor cellular morphological changes. Conclusions: Proposed approach based on the time-lapse images gained by DHM could improve the monitoring of live cell behavior in an automated fashion and could be further developed into a tool for high-throughput automated analysis of unique cell behavior.

中文翻译：

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通过机器学习自动解释延时定量相位图像，以研究上皮间质转化过程中的细胞动力学。

意义：机器学习越来越多地应用于微观数据的分类。为了检测一些复杂和动态的细胞过程，可能需要时间分辨活细胞成像。将时间信息并入分类过程可以允许更好和更具体的分类。目的：我们提出了一种基于数字全息显微镜 (DHM) 获得的延时定量相位图像 (QPI) 的细胞分类方法，目的是提高动态细胞过程分类的性能。方法：该方法是通过研究上皮间质转化 (EMT) 来证明的，EMT 需要主要且明显的时间依赖性形态变化。在 48 小时内获得 EMT 的延时 QPI，并提取代表动态细胞行为的特定新特征。两种不同的最终状态表型通过几种监督机器学习算法进行分类，并将结果与​​在单时间点图像上执行的分类进行比较。结果：与单时间点方法相比，我们的数据表明，在 EMT 期间将时间信息纳入细胞表型分类可将性能提高近 9%，并进一步表明 DHM 监测细胞的潜力形态变化。结论：