Cells respond heterogeneously to molecular and environmental perturbations. Phenotypic heterogeneity, wherein multiple phenotypes coexist in the same conditions, presents challenges when interpreting the observed heterogeneity. Advances in live cell microscopy allow researchers to acquire an unprecedented amount of live cell image data at high spatiotemporal resolutions. Phenotyping cellular dynamics, however, is a nontrivial task and requires machine learning (ML) approaches to discern phenotypic heterogeneity from live cell images. In recent years, ML has proven instrumental in biomedical research, allowing scientists to implement sophisticated computation in which computers learn and effectively perform specific analyses with minimal human instruction or intervention. In this review, we discuss how ML has been recently employed in the study of cell motility and morphodynamics to identify phenotypes from computer vision analysis. We focus on new approaches to extract and learn meaningful spatiotemporal features from complex live cell images for cellular and subcellular phenotyping.

细胞对分子和环境扰动的反应不同。表型异质性（即多种表型在相同条件下共存）在解释观察到的异质性时提出了挑战。活细胞显微镜的进步使研究人员能够以高时空分辨率获取前所未有的大量活细胞图像数据。然而，细胞动力学表型分析是一项艰巨的任务，需要机器学习 (ML) 方法从活细胞图像中辨别表型异质性。近年来，机器学习已被证明在生物医学研究中发挥着重要作用，它使科学家能够实现复杂的计算，让计算机在最少的人类指导或干预下学习并有效地执行特定分析。在这篇综述中，我们讨论了机器学习最近如何应用于细胞运动和形态动力学研究，以通过计算机视觉分析识别表型。我们专注于从复杂的活细胞图像中提取和学习有意义的时空特征以进行细胞和亚细胞表型分析的新方法。