Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments
Open accessAbstract
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.
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Nuclear segmentation was performed with DeepCell using in-house training and validation MIBI imaging datasets from a variety of human clinical tissue types to ensure reproducibility.
“Nuclear segmentation was performed with DeepCell (Van Valen et al., 2016) (https://github.com/vanvalenlab/deepcell-tf) as previously described (Keren et al., 2018), using in-house training and validation MIBI imaging datasets from a variety of human clinical tissue types to ensure reproducibility.”
From Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma (2020) · cites this paper as Van Valen et al., 2016
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- Authors
- Van Valen, D. A., Kudo, T., Lane, K. M., Macklin, D. N., Quach, N. T., DeFelice, M. M., Maayan, I., Tanouchi, Y., Ashley, E. A., & Covert, M. W.
- Affiliations
- Stanford University (US)
- Stanford Medicine (US)
- Corresponding author
- Covert, Markus W.
- ORCID
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-11-04
- Volume
- 12
- Issue
- 11
- Pages
- e1005177
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC5096676
- OpenAlex
- W2548342201
- Cited by
- 557
- References
- 71
- Citations by year
- 2026: 32 · 2025: 41 · 2024: 65 · 2023: 64 · 2022: 75 · 2021: 101 · 2020: 64 · 2019: 60 · 2018: 41 · 2017: 13 · 2016: 1
- FWCI
- 24.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
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- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Paul G. Allen Family Foundation (US) (Allen Distinguished Investigator, Allen Discovery Center)
- NIH Office of the Director (US) (5DP1LM01150)
- National Institute of General Medical Sciences (P50 GM107615, 1F32GM119319-01)
- U.S. Department of Energy (US) (DE-FG02-97ER25308)
- Siebel Foundation (Graduate Scholarship)
- Stanford University School of Medicine (US) (Stanford Biomedical Big Data Science Postdoctoral Fellowship)
- Burroughs Wellcome Fund (Postdoctoral Enrichment Program)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Keywords
- cell segmentation, live-cell imaging, convolutional neural networks, image segmentation, cellular heterogeneity, phase contrast imaging, cytoplasm segmentation, nuclei segmentation, bacterial cells, co-culture, automated image analysis, cell type annotation
- MeSH terms
- Intravital Microscopy, Machine Learning, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Neural Networks, Computer, Cell Tracking
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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