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• W2980113692 abstract "Abstract While live-cell imaging is a powerful approach for studying the dynamics of cellular systems, converting these imaging data into quantitative, single-cell records of cellular behavior has been a longstanding challenge. Deep learning methods have proven capable of performing cell segmentation—a critical task for analyzing live-cell imaging data—but their performance in cell tracking has been limited by a lack of dynamic datasets with temporally consistent single-cell labels. We bridge this gap through integrated development of labeling and deep learning methodology. We present a new framework for scalable, human-in-the-loop labeling of live-cell imaging movies, which we use to label a large collection of movies of fluorescently labeled cell nuclei. We use these data to create a new deep-learning-based cell-tracking method that achieves state-of-the-art performance in cell tracking. We have made all of the data, code, and software publicly available with permissive open-source licensing through the DeepCell project’s web portal https://deepcell.org ." @default.
• W2980113692 created "2019-10-18" @default.
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• W2980113692 date "2019-10-13" @default.
• W2980113692 modified "2023-10-18" @default.
• W2980113692 title "Caliban: Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning" @default.
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• W2980113692 doi "https://doi.org/10.1101/803205" @default.
• W2980113692 hasPublicationYear "2019" @default.
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