ArticleNature methods2019
U-Net: deep learning for cell counting, detection, and morphometry.
Article in Nature methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 632 papers, 1 of them a synthesis that pooled it.
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
632 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements in automated nuclei segmentation for histopathology using you only look once-driven approaches: A systematic review.Computers in biology and medicine · 2025Pooled it
- Broadband backscattering confocal microscopy enables label-free 3D live cell nanoscale sensitive imaging.Science advances · 2026Article
- Frequency-oriented adaptive real-time object detector for cluttered traffic scenes.Nature communications · 2026Article
- Deep learning-driven quantitative spectroscopic photoacoustic imaging for segmentation and oxygen saturation estimation.Ultrasonics · 2026Article
- Head-to-head comparison of pulsed-field ablation, high-power short-duration ablation, cryoballoon and conventional radiofrequency ablation by MRI-based ablation lesion assessment.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2026Article
- Artificial Intelligence-Empowered Single-Cell Phenotyping for Rapid, Automated Pathogen Diagnostics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Glucose repression ofbioRxiv : the preprint server for biology · 2026Article
- Deep learning-supported image quantification of epithelial cell shapes and its application to polycystic kidney disease.PLoS computational biology · 2026Article
- Hybrid deep reconstruction for vignetting-free upconversion imaging through scattering in epsilon-near-zero materials.Light, science & applications · 2026Article
- 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
- Photonic biosensors based on nanoparticle superstructures: from data analysis to artificial intelligence (AI) detection.Fundamental research · 2026Review
- From EM to AI: Multiscale imaging of the secretory pathway with insights from plant cells.Journal of microscopy · 2026Review
- Towards the construction of a virtual yeast.Nature · 2026Review
- PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns.bioRxiv : the preprint server for biology · 2026Article
- Direct Detection and Atomic Modeling of Ligands in Cryo-EM Maps Using Deep Learning.bioRxiv : the preprint server for biology · 2026Article
- LFCT: A Benchmark Dataset for Low-Frame-Rate Cell Tracking in Long-Term Live-Cell Microscopy.bioRxiv : the preprint server for biology · 2026Article
- Precise, predictable genome integrations by deep-learning-assisted design of microhomology-based templates.Nature biotechnology · 2026Article
- Deep-learning deconvolution and segmentation of fluorescent membranes for high-precision bacterial cell-size profiling.Communications biology · 2026Article
- Cell Structure Segmentation in TEM Images of Murine Skin Melanoma Cells by Deep Learning Model.Journal of imaging · 2026Article
572 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
U-Net is a generic deep-learning solution for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. We present an ImageJ plugin that enables non-machine-learning experts to analyze their data with U-Net on either a local computer or a remote server/cloud service. The plugin comes with pretrained models for single-cell segmentation and allows for U-Net to be adapted to new tasks on the basis of a few annotated samples.
Indexed as
Identifiers
30559429What 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.