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• W4380368679 abstract "Time-lapse microscopy is the only method that can directly capture the dynamics and heterogeneity of fundamental cellular processes at the single-cell level with high temporal resolution. Successful application of single-cell time-lapse microscopy requires automated segmentation and tracking of hundreds of individual cells over several time points. However, segmentation and tracking of single cells remain challenging for the analysis of time-lapse microscopy images, in particular for widely available and non-toxic imaging modalities such as phase-contrast imaging. This work presents a versatile and trainable deep-learning model, termed DeepSea, that allows for both segmentation and tracking of single cells in sequences of phase-contrast live microscopy images with higher precision than existing models. We showcase the application of DeepSea by analyzing cell size regulation in embryonic stem cells." @default.
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• W4380368679 date "2023-06-01" @default.
• W4380368679 modified "2023-09-27" @default.
• W4380368679 title "DeepSea is an efficient deep-learning model for single-cell segmentation and tracking in time-lapse microscopy" @default.
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• W4380368679 doi "https://doi.org/10.1016/j.crmeth.2023.100500" @default.
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