Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations

Dai Fei Elmer Ker, Sungeun Eom, Sho Sanami, Ryoma Bise, Corinne Pascale, Zhaozheng Yin, Seung Il Huh, Elvira Osuna-Highley, Silvina N. Junkers, Casey J. Helfrich, Peter Yongwen Liang, Jiyan Pan, Soojin Jeong, Steven S. Kang, Jinyu Liu, Ritchie Nicholson, Michael F. Sandbothe, Phu T. Van, Anan Liu, Mei ChenTakeo Kanade, Lee E. Weiss, Phil G. Campbell

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


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.

Original languageEnglish
Article number180237
JournalScientific Data
Publication statusPublished - 2018

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences


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