ArticlePLoS computational biology2016
Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 263 papers, 1 of them a synthesis that pooled it.
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Who cites it
263 citing papers in PubMed, 1 synthesis or guideline pooled it, 639 citations in OpenAlex.
- The immunoregulatory landscape of human tuberculosis granulomas.Nature immunology · 2022Pooled it
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- The dual roles of uterine monocytes in regulation of tissue homeostasis throughout reproductive cycles in health and disease.Nature immunology · 2026Article
- Independent tuning of outer membrane fluidity and mechanics in Gram-negative bacteria.bioRxiv : the preprint server for biology · 2026Article
- Follicular Lymphoma Transformation is Characterized by Cytokine-associated Remodeling of Stromal and Macrophage Compartments.Cancer discovery · 2026Article
- Same-Slide Spatial Multiomics Integration with IN-DEPTH Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment.Cancer discovery · 2026Article
- Data modeling the interplay between single-cell shape, single-cell protein expression, and tissue state.Cell reports methods · 2026Article
- Spotlight on challenges and novel methods in highly multiplexed tissue imaging-based spatial proteomics.Journal of translational medicine · 2026Review
- Segmentation-free analysis of live-cell imaging data reveals how T cell modifications influence cancer cell aggregation dynamics.Scientific reports · 2026Article
- Multi-region sampling of the human small intestine using an ingestible device.medRxiv : the preprint server for health sciences · 2026Article
- Coordinated immune-epithelial dynamics in the nasal epithelium protect against respiratory virus infection.bioRxiv : the preprint server for biology · 2026Article
- Deep-learning deconvolution and segmentation of fluorescent membranes for high-precision bacterial cell-size profiling.Communications biology · 2026Article
- Foundation model cascades enable zero-shot microscopy image analysis for cell therapy manufacturing.Cytotherapy · 2026Article
- Epstein-Barr virus orchestrates spatial reorganization and immunomodulation in the classic Hodgkin lymphoma tumor microenvironment.Cell reports. Medicine · 2026Article
- Persistent homology-based segmentation tool for membrane images.Cell reports methods · 2026Article
- Enhanced precision in cell culture analytics: leveraging artificial intelligence for unbiased and non-destructive assessment of cell growth and viability.Cell death discovery · 2026Article
- Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2026Article
- ColonyQuant: automated quantification and morphometric analysis of pluripotent stem cell colonies.BMC bioinformatics · 2026Article
- TNF-⍺-mediated myeloid-instructed CD14Cell reports. Medicine · 2026Article
- Theragenerative Nanomaterials: Integrating Therapy, Regeneration, and Diagnosis-Navigating the Shared Pathways Between Tissue Repair and Malignancy.International journal of nanomedicine · 2026Review
203 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.