ArticleNature methods2019
Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl.
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 239 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
239 citing papers in PubMed, 1 synthesis or guideline pooled it, 824 citations in OpenAlex.
- Systematic review of generative adversarial networks (GANs) in cell microscopy: Trends, practices, and impact on image augmentation.PloS one · 2025Pooled it
- Broadband backscattering confocal microscopy enables label-free 3D live cell nanoscale sensitive imaging.Science advances · 2026Article
- Neural Network-Based System for Rapid Diagnostic Decision Support Using FLIM Data: Segmentation and Classification in Liver, Skin, and Pancreatic Tissues.International journal of molecular sciences · 2026Article
- NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
- DIG-MambaNet: A Dual-Path Interactive Guided Mamba Network for Medical Image Segmentation.Journal of imaging · 2026Article
- Uncertainty Quantification in Medical Image Segmentation: A Comprehensive Survey.Journal of imaging · 2026Review
- Spotlight on challenges and novel methods in highly multiplexed tissue imaging-based spatial proteomics.Journal of translational medicine · 2026Review
- 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
- Cell shapes decode molecular phenotypes in image-based spatial proteomics.Cell systems · 2026Article
- 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
- Bridging annotated microscopy imaging data and analysis method development for scientific discovery.Patterns (New York, N.Y.) · 2026Review
- Maximum Matching Accuracy: An Instance Segmentation Evaluation Metric Utilizing Globally Optimal Matching.ArXiv · 2026Article
- A fused attention-based hybrid model for semi-supervised medical image segmentation.Biomedical engineering advances · 2026Article
- DyMamba: dynamic Mamba for microscopy image semantic segmentation.Bioinformatics (Oxford, England) · 2026Article
- NucVerse3D: generalizable 3D nuclear instance segmentation across heterogeneous microscopy modalities.Scientific reports · 2026Article
- Development of Human Conjunctival Goblet Cell Segmentation Datasets to Improve Quantitation.Scientific data · 2026Article
- Application of transformer models in medical image segmentation: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Synthetic data enables human-grade microtubule analysis with foundation models for segmentation.PLoS computational biology · 2026Article
- Automated Morphological Profiling via Deep Learning-Based Segmentation for High-Throughput Phenotypic Screening.Journal of imaging · 2026Article
179 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
13 authors at 1 institution in 1 country.
Funding
Abstract
Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative analysis of imaging data for biological and biomedical applications. Many bioimage analysis tools can segment nuclei in images but need to be selected and configured for every experiment. The 2018 Data Science Bowl attracted 3,891 teams worldwide to make the first attempt to build a segmentation method that could be applied to any two-dimensional light microscopy image of stained nuclei across experiments, with no human interaction. Top participants in the challenge succeeded in this task, developing deep-learning-based models that identified cell nuclei across many image types and experimental conditions without the need to manually adjust segmentation parameters. This represents an important step toward configuration-free bioimage analysis software tools.
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.