Time-lapse microscopy can directly capture the dynamics and heterogeneity of cellular processes at the single-cell level. Successful application of single-cell live microscopy requires automated segmentation and tracking of hundreds of individual cells over several time points. Recently, deep learning models have ushered in a new era in quantitative analysis of microscopy images. This work presents a versatile and trainable deep-learning-based software, termed DeepSea, that allows for both segmentation and tracking of single cells and their nuclei in sequences of phase-contrast live microscopy images. We show that DeepSea can quantify several cell biological features of mouse embryonic stem cells, such as cell division cycle, mitosis, cell morphology, and cell size, with high precision using phase-contrast images. Using DeepSea, we were able to show that despite their ultrafast cell division cycle, mouse embryonic stem cells exhibit cell size control in the G1 phase of the cell cycle.
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