ArticleF1000Research2020
Automated cell tracking using StarDist and TrackMate.
Article in F1000Research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed, 65 citations in OpenAlex.
- Review
- How to Train Custom Cell Segmentation Models Using Cell-APP.Bio-protocol · 2026Article
- Algorithmic analysis of the structure of mixed odontogenic tumors.Scientific reports · 2026Article
- Fast label-free live imaging with FlowVision reveals key principles of cancer cell arrest on endothelial monolayers.The EMBO journal · 2026Article
- Tui: A Multigenerational and Expert-Correctable Tracker for Cellular Dynamics.Computational and structural biotechnology journal · 2026Article
- Talin force coupling underlies eukaryotic cell-substrate adhesion.Nature communications · 2025Article
- From cells to pixels: A decision tree for designing bioimage analysis pipelines.Journal of microscopy · 2025Article
- Cell-APP: A generalizable method for cell annotation and cell-segmentation model training.Molecular biology of the cell · 2025Article
- Automated machine learning profiling with MAP-HR for quantifying homologous recombination foci in patient samples.NAR cancer · 2025Article
- Cell-APP: A generalizable method for cell annotation and cell-segmentation model training.bioRxiv : the preprint server for biology · 2025Article
- Low-Cost Microalgae Cell Concentration Estimation in Hydrochemistry Applications Using Computer Vision.Sensors (Basel, Switzerland) · 2025Article
- A Dual-Feature Framework for Enhanced Diagnosis of Myeloproliferative Neoplasm Subtypes Using Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2025Article
- Evaluation of image analysis tools for the measurement of cellular morphology.Frontiers in cell and developmental biology · 2025Review
- CellTracksColab is a platform that enables compilation, analysis, and exploration of cell tracking data.PLoS biology · 2024Article
- Svetlana a supervised segmentation classifier for Napari.Scientific reports · 2024Article
- PIEZO1 regulates leader cell formation and cellular coordination during collective keratinocyte migration.PLoS computational biology · 2024Article
- Transformer-based spatial-temporal detection of apoptotic cell death in live-cell imaging.eLife · 2024Article
- Article
- Computational approach for counting of SISH amplification signals for HER2 status assessment.PeerJ. Computer science · 2024Article
- A semi-automated cell tracking protocol for quantitative analyses of neutrophil swarming to sterile andbioRxiv : the preprint server for biology · 2023Article
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 6 institutions in 4 countries.
Funding
Abstract
The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.
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.