A modeling strategy for cell dynamic morphology classification based on local deformation patterns

Abstract Cell morphology is often used as an indicator of cell status to understand cell physiology. Therefore, the interpretation of cell dynamic morphology is a meaningful study in biomedical research. In this paper, a strategy based on local deformation patterns is introduced to classify cell dynamic morphology. The strategy decomposes dynamic morphology into local temporal features, and then captures local deformation patterns from these features through unsupervised learning. As the patterns contain underlying regularities of the dynamic morphology, they are employed to classify cell dynamic morphology. In our study, mouse lymphocytes were collected to observe the dynamic morphology, and two datasets were thus set up to investigate the performances of the proposed strategy. Experimental results validated the capacity of the proposed strategy. By considering the spatial heterogeneity and the temporal regularity of cell dynamic morphology, the strategy was competent to classify the dynamic morphology and provided remarkable advances in the accuracy and robustness of the classification on both datasets.