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Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images

In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived from cell phase images. Validation of the developed c...

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Autores principales: Belashov, Andrey V., Zhikhoreva, Anna A., Belyaeva, Tatiana N., Salova, Anna V., Kornilova, Elena S., Semenova, Irina V., Vasyutinskii, Oleg S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8533984/
https://www.ncbi.nlm.nih.gov/pubmed/34685568
http://dx.doi.org/10.3390/cells10102587
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author Belashov, Andrey V.
Zhikhoreva, Anna A.
Belyaeva, Tatiana N.
Salova, Anna V.
Kornilova, Elena S.
Semenova, Irina V.
Vasyutinskii, Oleg S.
author_facet Belashov, Andrey V.
Zhikhoreva, Anna A.
Belyaeva, Tatiana N.
Salova, Anna V.
Kornilova, Elena S.
Semenova, Irina V.
Vasyutinskii, Oleg S.
author_sort Belashov, Andrey V.
collection PubMed
description In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived from cell phase images. Validation of the developed classifier shows the accuracy for distinguishing between the three cell types of about 93% and between different cell states of the same cell line of about 89%. In the field test of the developed algorithm, we demonstrate successful evaluation of the temporal dynamics of relative amounts of live, apoptotic and necrotic cells after photodynamic treatment at different doses.
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spelling pubmed-85339842021-10-23 Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images Belashov, Andrey V. Zhikhoreva, Anna A. Belyaeva, Tatiana N. Salova, Anna V. Kornilova, Elena S. Semenova, Irina V. Vasyutinskii, Oleg S. Cells Article In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived from cell phase images. Validation of the developed classifier shows the accuracy for distinguishing between the three cell types of about 93% and between different cell states of the same cell line of about 89%. In the field test of the developed algorithm, we demonstrate successful evaluation of the temporal dynamics of relative amounts of live, apoptotic and necrotic cells after photodynamic treatment at different doses. MDPI 2021-09-29 /pmc/articles/PMC8533984/ /pubmed/34685568 http://dx.doi.org/10.3390/cells10102587 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Belashov, Andrey V.
Zhikhoreva, Anna A.
Belyaeva, Tatiana N.
Salova, Anna V.
Kornilova, Elena S.
Semenova, Irina V.
Vasyutinskii, Oleg S.
Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images
title Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images
title_full Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images
title_fullStr Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images
title_full_unstemmed Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images
title_short Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images
title_sort machine learning assisted classification of cell lines and cell states on quantitative phase images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8533984/
https://www.ncbi.nlm.nih.gov/pubmed/34685568
http://dx.doi.org/10.3390/cells10102587
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