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Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Erick Moen, Enrico Borba, Geneva Miller, Morgan Schwartz, Dylan Bannon, Nora Koe, Isabella Camplisson, Daniel Kyme, Cole Pavelchek, Tyler Price, Takamasa Kudo, Edward Pao, William Graf, David Van Valen
doi: https://doi.org/10.1101/803205
Erick Moen
1Division of Biology and Bioengineering, California Institute of Technology
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Enrico Borba
2Department of Computer Science, California Institute of Technology
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Geneva Miller
1Division of Biology and Bioengineering, California Institute of Technology
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Morgan Schwartz
1Division of Biology and Bioengineering, California Institute of Technology
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Dylan Bannon
1Division of Biology and Bioengineering, California Institute of Technology
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Nora Koe
3Department of Electrical Engineering, California Institute of Technology
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Isabella Camplisson
2Department of Computer Science, California Institute of Technology
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Daniel Kyme
2Department of Computer Science, California Institute of Technology
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Cole Pavelchek
4Department of Neurosciences, University of California, San Diego
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Tyler Price
1Division of Biology and Bioengineering, California Institute of Technology
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Takamasa Kudo
5Department of Chemical and Systems Biology, Stanford University
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Edward Pao
1Division of Biology and Bioengineering, California Institute of Technology
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William Graf
1Division of Biology and Bioengineering, California Institute of Technology
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David Van Valen
1Division of Biology and Bioengineering, California Institute of Technology
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  • For correspondence: vanvalen@caltech.edu
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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 on 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.

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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.
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Posted October 14, 2019.
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Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning
Erick Moen, Enrico Borba, Geneva Miller, Morgan Schwartz, Dylan Bannon, Nora Koe, Isabella Camplisson, Daniel Kyme, Cole Pavelchek, Tyler Price, Takamasa Kudo, Edward Pao, William Graf, David Van Valen
bioRxiv 803205; doi: https://doi.org/10.1101/803205
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Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning
Erick Moen, Enrico Borba, Geneva Miller, Morgan Schwartz, Dylan Bannon, Nora Koe, Isabella Camplisson, Daniel Kyme, Cole Pavelchek, Tyler Price, Takamasa Kudo, Edward Pao, William Graf, David Van Valen
bioRxiv 803205; doi: https://doi.org/10.1101/803205

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