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.
Recommended Citation
D. F. Ker et al., "Phase Contrast Time-Lapse Microscopy Datasets with Automated and Manual Cell Tracking Annotations," Scientific Data, vol. 5, Nature Publishing Group, Nov 2018.
The definitive version is available at https://doi.org/10.1038/sdata.2018.237
Department(s)
Computer Science
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Algorithms; Animal; Cell line; Cytology; Mouse; Myoblast; Phase contrast microscopy; Procedures; Time lapse imaging; Cell Tracking; Mice; Microscopy; Phase-Contrast; Myoblasts; Time-Lapse Imaging
International Standard Serial Number (ISSN)
2052-4463
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2018 The Author(s), All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Nov 2018
PubMed ID
30422120
Comments
This work was supported by NIH grants RO1EB004343 and RO1EB007369 as well as funding from the Pennsylvania Infrastructure Technology Alliance (PITA).