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

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作者
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 Chen
Takeo Kanade
Lee E. Weiss
Phil G. Campbell
机构
[1] Carnegie Mellon University,Department of Biological Sciences
[2] Institute for Tissue Engineering and Regenerative Medicine,Department of Advanced Information Technology
[3] The Chinese University of Hong Kong,Department of Computer Science
[4] School of Biomedical Sciences,Department of Computer Science
[5] Faculty of Medicine,Department of Electrical and Computer Engineering
[6] The Chinese University of Hong Kong,Department of Biomedical Engineering
[7] Robotics Institute,undefined
[8] Carnegie Mellon University,undefined
[9] Dai Nippon Printing,undefined
[10] Kyushu University,undefined
[11] Engineering Research Accelerator,undefined
[12] Carnegie Mellon University,undefined
[13] Missouri University of Science and Technology,undefined
[14] Intel Labs Pittsburgh,undefined
[15] Carnegie Mellon University,undefined
[16] School of Electrical and Information Engineering,undefined
[17] Tianjin University,undefined
[18] University at Albany,undefined
[19] State University of New York,undefined
[20] Carnegie Mellon University,undefined
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摘要
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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