ArticleeLife2020
CytoCensus, mapping cell identity and division in tissues and organs using machine learning.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 32 citations in OpenAlex.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- A Framework for Automated Tracking of Morphologically Distinct Cell Populations in Time-Lapse Microscopy....IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies · 2026Article
- Deep3DSIM: Super-resolution imaging of thick tissue using 3D structured illumination with adaptive optics.eLife · 2025Article
- Imp/IGF2BP and Syp/SYNCRIP temporal RNA interactomes uncover combinatorial networks of regulators ofScience advances · 2025Article
- The paradigm shift in neural stem cells basic research driven by artificial intelligence related technologies.Frontiers in cellular neuroscience · 2025Review
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- Image restoration of degraded time-lapse microscopy data mediated by near-infrared imaging.Nature methods · 2024Article
- Dynamically regulated transcription factors are encoded by highly unstable mRNAs in theRNA (New York, N.Y.) · 2023Article
- Human Stem Cells for Ophthalmology: Recent Advances in Diagnostic Image Analysis and Computational Modelling.Current stem cell reports · 2023Review
- A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations.PeerJ. Computer science · 2023Article
- Retinal Organoid Technology: Where Are We Now?International journal of molecular sciences · 2021Review
- Deep learning for bioimage analysis in developmental biology.Development (Cambridge, England) · 2021Review
- Recent Trends and Perspectives in Cerebral Organoids Imaging and Analysis.Frontiers in neuroscience · 2021Review
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends convenient 2D 'point-and-click' user training to 3D detection of cells in challenging datasets with ill-defined cell boundaries. In tests on such datasets, CytoCensus outperforms other freely available image analysis software in accuracy and speed of cell detection. We used CytoCensus to count stem cells and their progeny, and to quantify individual cell divisions from time-lapse movies of explanted
Indexed as
Identifiers
What OpenQuestion holds
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.