Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

Van Valen, David A.; Kudo, Takamasa; Lane, Keara M.; Macklin, Derek N.; Quach, Nicolas T.; DeFelice, Mialy M.; Maayan, Inbal; Tanouchi, Yu; Ashley, Euan A.; Covert, Markus W.

doi:10.1371/journal.pcbi.1005177

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

Journal Article · Fri Nov 04 00:00:00 EDT 2016 · PLoS Computational Biology (Online)

DOI:https://doi.org/10.1371/journal.pcbi.1005177· OSTI ID:1425613

Van Valen, David A. ^[1]; ^[2]; Lane, Keara M. ^[3]; ^[3]; Quach, Nicolas T. ^[3]; ^[3]; Maayan, Inbal ^[3]; ^[3]; ^[4]; ^[5]

Stanford Univ., CA (United States). Dept. of Bioengineering; Department of Bioengineering, Stanford University, Stanford, California
Stanford Univ., CA (United States). Dept. of Chemical and Systems Biology
Stanford Univ., CA (United States). Dept. of Bioengineering
Stanford Univ., CA (United States). Dept. of Genetics. Dept. of Cardiovascular Medicine
Stanford Univ., CA (United States). Dept. of Bioengineering. Dept. of Chemical and Systems Biology

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

View Accepted Manuscript (DOE)

Research Organization:: Stanford Univ., CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); National Inst. of Health (NIH) (United States)

Grant/Contract Number:: FG02-97ER25308

OSTI ID:: 1425613

Journal Information:: PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Journal Issue: 11 Vol. 12; ISSN 1553-7358

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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Computational approaches for characterizing the tumor immune microenvironment Liu, Candace C.; Steen, Chloé B.; Newman, Aaron M. Immunology, Vol. 158, Issue 2 https://doi.org/10.1111/imm.13101	journal	August 2019
Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison Vicar, Tomas; Balvan, Jan; Jaros, Josef BMC Bioinformatics, Vol. 20, Issue 1 https://doi.org/10.1186/s12859-019-2880-8	journal	June 2019
Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets Hay, Edouard A.; Parthasarathy, Raghuveer PLOS Computational Biology, Vol. 14, Issue 12 https://doi.org/10.1371/journal.pcbi.1006628	journal	December 2018
A novel machine learning based approach for iPS progenitor cell identification Zhang, Haishan; Shao, Ximing; Peng, Yin PLOS Computational Biology, Vol. 15, Issue 12 https://doi.org/10.1371/journal.pcbi.1007351	journal	December 2019
Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells Kavitha, Muthu Subash; Kurita, Takio; Park, Soon-Yong PLOS ONE, Vol. 12, Issue 12 https://doi.org/10.1371/journal.pone.0189974	journal	December 2017
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Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure. Laine, Romain F.; Goodfellow, Gemma; Young, Laurence J. Apollo - University of Cambridge Repository https://doi.org/10.17863/cam.34802	text	January 2018
Artificial Intelligence for Microscopy: What You Should Know von Chamier, Lucas; Laine, Romain F.; Henriques, Ricardo https://doi.org/10.20944/preprints201902.0004.v1	posted_content	February 2019
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Deep Learning for Non-Invasive Determination of the Differentiation Status of Human Neuronal Cells by Using Phase-Contrast Photomicrographs Ooka, Maya; Tokuoka, Yuta; Nishimoto, Shori Applied Sciences, Vol. 9, Issue 24 https://doi.org/10.3390/app9245503	journal	December 2019
Learning from droplet flows in microfluidic channels using deep neural networks Pooria, Hadikhani,; Navid, Borhani,; H., Hashemi, S. Mohammad ETH Zurich https://doi.org/10.3929/ethz-b-000346527	text	January 2019
Convolutional neural networks automate detection for tracking of submicron scale particles in 2D and 3D Newby, Jay M.; Schaefer, Alison M.; Lee, Phoebe T. arXiv https://doi.org/10.48550/arxiv.1704.03009	text	January 2017
Investigating Epithelial-To-Mesenchymal Transition with Integrated Computational and Experimental Approaches Xing, Jianhua; Tian, Xiao-Jun arXiv https://doi.org/10.48550/arxiv.1901.06239	text	January 2019
Deep learning in bioinformatics: introduction, application, and perspective in big data era Li, Yu; Huang, Chao; Ding, Lizhong arXiv https://doi.org/10.48550/arxiv.1903.00342	preprint	January 2019
On the objectivity, reliability, and validity of deep learning enabled bioimage analyses Segebarth, Dennis; Griebel, Matthias; Stein, Nikolai eLife, Vol. 9 https://doi.org/10.7554/eLife.59780	journal	October 2020
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