Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning
Moen, E.; Borba, E.; Miller, G.; Schwartz, M.; Bannon, D.; Koe, N.; Camplisson, I.; Kyme, D.; Pavelchek, C.; Price, T.; Kudo, T.; Pao, E.; Graf, W.; Van Valen, D. A.
Show abstract
While live-cell imaging is a powerful approach to 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 the 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 projects web portal https://deepcell.org.
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