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Published October 14, 2019 | Submitted
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Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Abstract

Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org.

Additional Information

The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. bioRxiv preprint first posted online Oct. 13, 2019. We thank Anima Anandkumar, Michael Angelo, Michael Elowitz, Christopher Frick, Lea Geontoro, Kerwyn Casey Huang, and Gregory Johnson for helpful suggestions and sharing data. We thank Ian Brown and Andy Butkovic for assistance using the Figure 8 image annotation platform, as well as numerous anonymous annotators whose efforts enabled this work. We also thank Henrietta Lacks for graciously donating source material. We gratefully acknowledge support from the Paul Allen Family Foundation through the Discovery Centers at Stanford University and Caltech, The Rosen Center for Bioengineering at Caltech, The Center for Environmental and Microbial Interactions at Caltech, Google Research Cloud, Figure 8's AI for everyone award, and a subaward from NIH U24CA224309-01. Author contributions: EM, WG, and DVV conceived of the project; EM, EB, MS, DB, WG, and DVV designed and wrote the cell tracking algorithm and its deployment; EB, EM, and GM designed and wrote the Caliban software; GM designed and oversaw the data annotation; GM, NK, IC, DK, CP, and TP annotated data; MS, EM, and CP designed and performed benchmarking; TK and EP collected data for annotation; EM and DVV wrote the paper; DVV supervised the project. Datasets: All of the data used in this paper and the associated annotations can be accessed at http://www.deepcell.org/data or at http://www.github.com/vanvalenlab through the datasets module. Source code: A persistent deployment of the software described here can be accessed at http://www.deepcell.org. All source code for cell tracking is available in the DeepCell repository at http://www.github.com/vanvalenlab/deepcell-tf. The source code for the Caliban software is available at http://www.github.com/vanvalenlab/Caliban. Detailed instructions are available at http://deepcell.readthedocs.io/. Competing interests: The authors have filed a provisional patent for the described work; the software described here is available under a modified Apache license and is free for non-commercial uses.

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Created:
August 19, 2023
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October 18, 2023