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Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations
- Source :
- Scientific Data
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
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. Machine-accessible metadata file describing the reported data (ISA-Tab format)
- Subjects :
- 0301 basic medicine
Statistics and Probability
Data Descriptor
Computer science
Phase contrast microscopy
Population
Phase-contrast microscopy
Library and Information Sciences
Tracking (particle physics)
Time-Lapse Imaging
Manual curation
Time-lapse microscopy
Cell Line
030218 nuclear medicine & medical imaging
Education
law.invention
Myoblasts
Mice
03 medical and health sciences
0302 clinical medicine
law
Animals
Microscopy, Phase-Contrast
education
education.field_of_study
business.industry
Pattern recognition
Computational biology and bioinformatics
Computer Science Applications
Metadata
030104 developmental biology
Cell Tracking
Benchmark (computing)
Cell tracking
Artificial intelligence
Statistics, Probability and Uncertainty
business
Algorithms
Information Systems
Subjects
Details
- ISSN :
- 20524463
- Volume :
- 5
- Database :
- OpenAIRE
- Journal :
- Scientific Data
- Accession number :
- edsair.doi.dedup.....65cdb2aa95f44a6ea3e71aa567c9ab80