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Development of image analysis software for quantification of viable cells in microchips
Over the past few years, image analysis has emerged as a powerful tool for analyzing various cell biology parameters in an unprecedented and highly specific manner. The amount of data that is generated requires automated methods for the processing and analysis of all the resulting information. The s...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5832319/ https://www.ncbi.nlm.nih.gov/pubmed/29494694 http://dx.doi.org/10.1371/journal.pone.0193605 |
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author | Georg, Maximilian Fernández-Cabada, Tamara Bourguignon, Natalia Karp, Paola Peñaherrera, Ana B. Helguera, Gustavo Lerner, Betiana Pérez, Maximiliano S. Mertelsmann, Roland |
author_facet | Georg, Maximilian Fernández-Cabada, Tamara Bourguignon, Natalia Karp, Paola Peñaherrera, Ana B. Helguera, Gustavo Lerner, Betiana Pérez, Maximiliano S. Mertelsmann, Roland |
author_sort | Georg, Maximilian |
collection | PubMed |
description | Over the past few years, image analysis has emerged as a powerful tool for analyzing various cell biology parameters in an unprecedented and highly specific manner. The amount of data that is generated requires automated methods for the processing and analysis of all the resulting information. The software available so far are suitable for the processing of fluorescence and phase contrast images, but often do not provide good results from transmission light microscopy images, due to the intrinsic variation of the acquisition of images technique itself (adjustment of brightness / contrast, for instance) and the variability between image acquisition introduced by operators / equipment. In this contribution, it has been presented an image processing software, Python based image analysis for cell growth (PIACG), that is able to calculate the total area of the well occupied by cells with fusiform and rounded morphology in response to different concentrations of fetal bovine serum in microfluidic chips, from microscopy images in transmission light, in a highly efficient way. |
format | Online Article Text |
id | pubmed-5832319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-58323192018-03-23 Development of image analysis software for quantification of viable cells in microchips Georg, Maximilian Fernández-Cabada, Tamara Bourguignon, Natalia Karp, Paola Peñaherrera, Ana B. Helguera, Gustavo Lerner, Betiana Pérez, Maximiliano S. Mertelsmann, Roland PLoS One Research Article Over the past few years, image analysis has emerged as a powerful tool for analyzing various cell biology parameters in an unprecedented and highly specific manner. The amount of data that is generated requires automated methods for the processing and analysis of all the resulting information. The software available so far are suitable for the processing of fluorescence and phase contrast images, but often do not provide good results from transmission light microscopy images, due to the intrinsic variation of the acquisition of images technique itself (adjustment of brightness / contrast, for instance) and the variability between image acquisition introduced by operators / equipment. In this contribution, it has been presented an image processing software, Python based image analysis for cell growth (PIACG), that is able to calculate the total area of the well occupied by cells with fusiform and rounded morphology in response to different concentrations of fetal bovine serum in microfluidic chips, from microscopy images in transmission light, in a highly efficient way. Public Library of Science 2018-03-01 /pmc/articles/PMC5832319/ /pubmed/29494694 http://dx.doi.org/10.1371/journal.pone.0193605 Text en © 2018 Georg et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Georg, Maximilian Fernández-Cabada, Tamara Bourguignon, Natalia Karp, Paola Peñaherrera, Ana B. Helguera, Gustavo Lerner, Betiana Pérez, Maximiliano S. Mertelsmann, Roland Development of image analysis software for quantification of viable cells in microchips |
title | Development of image analysis software for quantification of viable cells in microchips |
title_full | Development of image analysis software for quantification of viable cells in microchips |
title_fullStr | Development of image analysis software for quantification of viable cells in microchips |
title_full_unstemmed | Development of image analysis software for quantification of viable cells in microchips |
title_short | Development of image analysis software for quantification of viable cells in microchips |
title_sort | development of image analysis software for quantification of viable cells in microchips |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5832319/ https://www.ncbi.nlm.nih.gov/pubmed/29494694 http://dx.doi.org/10.1371/journal.pone.0193605 |
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