Caliban: Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning (original) (raw)

New Results

, Erick Moen, Geneva Miller, Tom Dougherty, Enrico Borba, Rachel Ding, William Graf, Edward Pao, David Van Valen

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 project’s web portal https://deepcell.org.

Competing Interest Statement

David Van Valen is a co-founder and Chief Scientist of Barrier Biosciences and holds equity in the company. All other authors declare no competing interests.

Footnotes

New results and figures - we have included benchmarking on data provided by the cell tracking challenge.
https://deepcell.readthedocs.io/en/master/data-gallery/index.html

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