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Bioimage Analysis and Cell Motility

Bioimage analysis (BIA) has historically helped study how and why cells move; biological experiments evolved in intimate feedback with the most classical image processing techniques because they contribute objectivity and reproducibility to an eminently qualitative science. Cell segmentation, tracki...

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Detalles Bibliográficos
Autores principales: Boquet-Pujadas, Aleix, Olivo-Marin, Jean-Christophe, Guillén, Nancy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7815951/
https://www.ncbi.nlm.nih.gov/pubmed/33511365
http://dx.doi.org/10.1016/j.patter.2020.100170
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author Boquet-Pujadas, Aleix
Olivo-Marin, Jean-Christophe
Guillén, Nancy
author_facet Boquet-Pujadas, Aleix
Olivo-Marin, Jean-Christophe
Guillén, Nancy
author_sort Boquet-Pujadas, Aleix
collection PubMed
description Bioimage analysis (BIA) has historically helped study how and why cells move; biological experiments evolved in intimate feedback with the most classical image processing techniques because they contribute objectivity and reproducibility to an eminently qualitative science. Cell segmentation, tracking, and morphology descriptors are all discussed here. Using ameboid motility as a case study, these methods help us illustrate how proper quantification can augment biological data, for example, by choosing mathematical representations that amplify initially subtle differences, by statistically uncovering general laws or by integrating physical insight. More recently, the non-invasive nature of quantitative imaging is fertilizing two blooming fields: mechanobiology, where many biophysical measurements remain inaccessible, and microenvironments, where the quest for physiological relevance has exploded data size. From relief to remedy, this trend indicates that BIA is to become a main vector of biological discovery as human visual analysis struggles against ever more complex data.
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spelling pubmed-78159512021-01-27 Bioimage Analysis and Cell Motility Boquet-Pujadas, Aleix Olivo-Marin, Jean-Christophe Guillén, Nancy Patterns (N Y) Review Bioimage analysis (BIA) has historically helped study how and why cells move; biological experiments evolved in intimate feedback with the most classical image processing techniques because they contribute objectivity and reproducibility to an eminently qualitative science. Cell segmentation, tracking, and morphology descriptors are all discussed here. Using ameboid motility as a case study, these methods help us illustrate how proper quantification can augment biological data, for example, by choosing mathematical representations that amplify initially subtle differences, by statistically uncovering general laws or by integrating physical insight. More recently, the non-invasive nature of quantitative imaging is fertilizing two blooming fields: mechanobiology, where many biophysical measurements remain inaccessible, and microenvironments, where the quest for physiological relevance has exploded data size. From relief to remedy, this trend indicates that BIA is to become a main vector of biological discovery as human visual analysis struggles against ever more complex data. Elsevier 2021-01-08 /pmc/articles/PMC7815951/ /pubmed/33511365 http://dx.doi.org/10.1016/j.patter.2020.100170 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Review
Boquet-Pujadas, Aleix
Olivo-Marin, Jean-Christophe
Guillén, Nancy
Bioimage Analysis and Cell Motility
title Bioimage Analysis and Cell Motility
title_full Bioimage Analysis and Cell Motility
title_fullStr Bioimage Analysis and Cell Motility
title_full_unstemmed Bioimage Analysis and Cell Motility
title_short Bioimage Analysis and Cell Motility
title_sort bioimage analysis and cell motility
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7815951/
https://www.ncbi.nlm.nih.gov/pubmed/33511365
http://dx.doi.org/10.1016/j.patter.2020.100170
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