1. Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations
- Author
-
Dai Fei Elmer Ker, Jiyan Pan, Casey J. Helfrich, Phil G. Campbell, Jinyu Liu, Ryoma Bise, Zhaozheng Yin, Ritchie Nicholson, Peter Liang, Sho Sanami, Michael F. Sandbothe, Silvina N. Junkers, An-An Liu, Sungeun Eom, Phu T. Van, Soojin Jeong, Steven S. Kang, Seungil Huh, Takeo Kanade, Lee E. Weiss, Elvira Osuna-Highley, Mei Chen, and Corinne Pascale
- 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 - 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)
- Published
- 2018