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Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations
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 seq...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6233481/ https://www.ncbi.nlm.nih.gov/pubmed/30422120 http://dx.doi.org/10.1038/sdata.2018.237 |
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author | Ker, Dai Fei Elmer Eom, Sungeun Sanami, Sho Bise, Ryoma Pascale, Corinne Yin, Zhaozheng Huh, Seung-il Osuna-Highley, Elvira Junkers, Silvina N. Helfrich, Casey J. Liang, Peter Yongwen Pan, Jiyan Jeong, Soojin Kang, Steven S. Liu, Jinyu Nicholson, Ritchie Sandbothe, Michael F. Van, Phu T. Liu, Anan Chen, Mei Kanade, Takeo Weiss, Lee E. Campbell, Phil G. |
author_facet | Ker, Dai Fei Elmer Eom, Sungeun Sanami, Sho Bise, Ryoma Pascale, Corinne Yin, Zhaozheng Huh, Seung-il Osuna-Highley, Elvira Junkers, Silvina N. Helfrich, Casey J. Liang, Peter Yongwen Pan, Jiyan Jeong, Soojin Kang, Steven S. Liu, Jinyu Nicholson, Ritchie Sandbothe, Michael F. Van, Phu T. Liu, Anan Chen, Mei Kanade, Takeo Weiss, Lee E. Campbell, Phil G. |
author_sort | Ker, Dai Fei Elmer |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6233481 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-62334812018-11-14 Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations Ker, Dai Fei Elmer Eom, Sungeun Sanami, Sho Bise, Ryoma Pascale, Corinne Yin, Zhaozheng Huh, Seung-il Osuna-Highley, Elvira Junkers, Silvina N. Helfrich, Casey J. Liang, Peter Yongwen Pan, Jiyan Jeong, Soojin Kang, Steven S. Liu, Jinyu Nicholson, Ritchie Sandbothe, Michael F. Van, Phu T. Liu, Anan Chen, Mei Kanade, Takeo Weiss, Lee E. Campbell, Phil G. Sci Data Data Descriptor 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. Nature Publishing Group 2018-11-13 /pmc/articles/PMC6233481/ /pubmed/30422120 http://dx.doi.org/10.1038/sdata.2018.237 Text en Copyright © 2018, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article. |
spellingShingle | Data Descriptor Ker, Dai Fei Elmer Eom, Sungeun Sanami, Sho Bise, Ryoma Pascale, Corinne Yin, Zhaozheng Huh, Seung-il Osuna-Highley, Elvira Junkers, Silvina N. Helfrich, Casey J. Liang, Peter Yongwen Pan, Jiyan Jeong, Soojin Kang, Steven S. Liu, Jinyu Nicholson, Ritchie Sandbothe, Michael F. Van, Phu T. Liu, Anan Chen, Mei Kanade, Takeo Weiss, Lee E. Campbell, Phil G. Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations |
title | Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations |
title_full | Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations |
title_fullStr | Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations |
title_full_unstemmed | Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations |
title_short | Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations |
title_sort | phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6233481/ https://www.ncbi.nlm.nih.gov/pubmed/30422120 http://dx.doi.org/10.1038/sdata.2018.237 |
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