ReviewNature methods2019
Deep learning for cellular image analysis.
Review in Nature methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 493 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
493 citing papers in PubMed.
- A Deep Learning Pipeline for Cell Segmentation and Viability Quantification in 3D Constructs From Fluorescence Microscopy Images.Microscopy research and technique · 2026Article
- SIGMA: SIgnaling via Golgi Morphology Analysis for pathway-state classification.bioRxiv : the preprint server for biology · 2026Article
- 3D multi-omics tumour atlases: from technology to biology and clinical translation.Nature reviews. Cancer · 2026Review
- Lipid tail length dictates membrane disruption by carmofur analogs and reveals noncanonical modes of action.RSC advances · 2026Article
- Review
- Non-equilibrium crystallization and spatial fingerprints in flash IR-annealed halide perovskite films.Materials horizons · 2026Article
- An optogenetic smart microscopy platform reveals signaling dynamics-dependent control over collective cell migration.Cell systems · 2026Article
- High-throughput virus quantification using cytopathic effect area analysis.PLoS computational biology · 2026Article
- Artificial Intelligence-Driven Reproductive Bioengineering: Integrating Fertility Diagnostics, Organ-on-Chip Systems, Cryobiology and Epigenetic Safety for Precision Reproductive Medicine.Bioengineering (Basel, Switzerland) · 2026Review
- Transforming subcellular spatial transcriptomics: deep learning models for cell segmentation.Nucleic acids research · 2026Review
- Urinary Cell-Free DNA detection platform for bacterial cystitis diagnosis via spot-based machine learning.Journal of advanced research · 2026Article
- Detection of malaria infection from parasite-free blood smears.Malaria journal · 2026Article
- From stars to molecules: AI guided device-agnostic super-resolution imaging.Nature communications · 2026Article
- Article
- Celldetective, an AI-enhanced image analysis tool for unraveling dynamic cell interactions.eLife · 2026Article
- Microscopy Cell Segmentation: Review and Benchmarking of Task-Specific and Foundation Models.Journal of imaging · 2026Review
- From EM to AI: Multiscale imaging of the secretory pathway with insights from plant cells.Journal of microscopy · 2026Review
- A quantitative coordinate system for developmental dynamics.bioRxiv : the preprint server for biology · 2026Article
- Image-based phenotypic sorting of synthetic cells.Science advances · 2026Article
- TransNet-SAM2: A Transformer-Foundation Model Framework for Prompt-Free Segmentation of White Blood Cells in Microscopic Blood Smear Images.Diagnostics (Basel, Switzerland) · 2026Article
433 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
6 authors.
Funding
Abstract
Recent advances in computer vision and machine learning underpin a collection of algorithms with an impressive ability to decipher the content of images. These deep learning algorithms are being applied to biological images and are transforming the analysis and interpretation of imaging data. These advances are positioned to render difficult analyses routine and to enable researchers to carry out new, previously impossible experiments. Here we review the intersection between deep learning and cellular image analysis and provide an overview of both the mathematical mechanics and the programming frameworks of deep learning that are pertinent to life scientists. We survey the field's progress in four key applications: image classification, image segmentation, object tracking, and augmented microscopy. Last, we relay our labs' experience with three key aspects of implementing deep learning in the laboratory: annotating training data, selecting and training a range of neural network architectures, and deploying solutions. We also highlight existing datasets and implementations for each surveyed application.
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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.