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 Chen; Takeo Kanade; Lee E. Weiss; Phil G. Campbell

doi:10.1038/sdata.2018.237

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

Practical Data Science

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30 Citations (Scopus)

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.

Original language	English
Article number	180237
Journal	Scientific Data
Volume	5
DOIs	https://doi.org/10.1038/sdata.2018.237
Publication status	Published - 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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10.1038/sdata.2018.237

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Ker, D. F. E., Eom, S., Sanami, S., Bise, R., Pascale, C., Yin, Z., Huh, S. I., Osuna-Highley, E., Junkers, S. N., Helfrich, C. J., Liang, P. Y., Pan, J., Jeong, S., Kang, S. S., Liu, J., Nicholson, R., Sandbothe, M. F., Van, P. T., Liu, A., ... Campbell, P. G. (2018). Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations. Scientific Data, 5, Article 180237. https://doi.org/10.1038/sdata.2018.237

Ker, DFE, Eom, S, Sanami, S, Bise, R, Pascale, C, Yin, Z, Huh, SI, Osuna-Highley, E, Junkers, SN, Helfrich, CJ, Liang, PY, Pan, J, Jeong, S, Kang, SS, Liu, J, Nicholson, R, Sandbothe, MF, Van, PT, Liu, A, Chen, M, Kanade, T, Weiss, LE & Campbell, PG 2018, 'Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations', Scientific Data, vol. 5, 180237. https://doi.org/10.1038/sdata.2018.237

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title = "Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations",

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.",

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

note = "Funding Information: This work was supported by NIH grants RO1EB004343 and RO1EB007369 as well as funding from the Pennsylvania Infrastructure Technology Alliance (PITA). Publisher Copyright: {\textcopyright} The Author(s) 2018.",

year = "2018",

doi = "10.1038/sdata.2018.237",

language = "English",

volume = "5",

journal = "Scientific Data",

issn = "2052-4463",

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AU - Ker, Dai Fei Elmer

AU - Eom, Sungeun

AU - Sanami, Sho

AU - Bise, Ryoma

AU - Pascale, Corinne

AU - Yin, Zhaozheng

AU - Huh, Seung Il

AU - Osuna-Highley, Elvira

AU - Junkers, Silvina N.

AU - Helfrich, Casey J.

AU - Liang, Peter Yongwen

AU - Pan, Jiyan

AU - Jeong, Soojin

AU - Kang, Steven S.

AU - Liu, Jinyu

AU - Nicholson, Ritchie

AU - Sandbothe, Michael F.

AU - Van, Phu T.

AU - Liu, Anan

AU - Chen, Mei

AU - Kanade, Takeo

AU - Weiss, Lee E.

AU - Campbell, Phil G.

N1 - Funding Information: This work was supported by NIH grants RO1EB004343 and RO1EB007369 as well as funding from the Pennsylvania Infrastructure Technology Alliance (PITA). Publisher Copyright: © The Author(s) 2018.

PY - 2018

Y1 - 2018

N2 - 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.

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

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