ArticleScientific data2018
Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations.
Article in Scientific data, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Label-free TIMING: an efficient, reliable and scalable AI workflow for automated profiling of cell-cell interaction behaviors in nanowell arrays.Frontiers in bioinformatics · 2026Article
- Label-Free Cancer Detection Methods Based on Biophysical Cell Phenotypes.Bioengineering (Basel, Switzerland) · 2025Review
- Comparative Study of Cell Nuclei Segmentation Based on Computational and Handcrafted Features Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2025Article
- Explainable handcrafted features for mitotic event detection and classification.Scientific reports · 2025Article
- Cellular behavior analysis from live-cell imaging of TCR T cell-cancer cell interactions.bioRxiv : the preprint server for biology · 2024Article
- Label-free live cell recognition and tracking for biological discoveries and translational applications.Npj imaging · 2024Review
- CMTT-JTracker: a fully test-time adaptive framework serving automated cell lineage construction.Briefings in bioinformatics · 2024Article
- Exploring Label-Free Imaging Techniques with Copper Sulfide Microspheres for Observing Breast Cancer Cells.ACS omega · 2024Article
- Differentiable optimization layers enhance GNN-based mitosis detection.Scientific reports · 2023Article
- DeepSea is an efficient deep-learning model for single-cell segmentation and tracking in time-lapse microscopy.Cell reports methods · 2023Article
- LapTrack: linear assignment particle tracking with tunable metrics.Bioinformatics (Oxford, England) · 2023Article
- An open-source, end-to-end workflow for multidimensional photoemission spectroscopy.Scientific data · 2020Article
- Virtual organelle self-coding for fluorescence imaging via adversarial learning.Journal of biomedical optics · 2020Article
- Kinetic Analysis of the Motility of Giant Virus-Infected Amoebae Using Phase-Contrast Microscopic Images.Frontiers in microbiology · 2019Article
Corrections and comments
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Authors and funding
23 authors.
Funding
Abstract
Phase contrast time-lapse microscopy is a non-destructive technique that generates large volumes of image-based information to quantify the behaviour of individual cells or cell populations. To guide the development of algorithms for computer-aided cell tracking and analysis, 48 time-lapse image sequences, each spanning approximately 3.5 days, were generated with accompanying ground truths for C2C12 myoblast cells cultured under 4 different media conditions, including with fibroblast growth factor 2 (FGF2), bone morphogenetic protein 2 (BMP2), FGF2 + BMP2, and control (no growth factor). The ground truths generated contain information for tracking at least 3 parent cells and their descendants within these datasets and were validated using a two-tier system of manual curation. This comprehensive, validated dataset will be useful in advancing the development of computer-aided cell tracking algorithms and function as a benchmark, providing an invaluable opportunity to deepen our understanding of individual and population-based cell dynamics for biomedical research.
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Registered trials
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