Cell quantification in digital contrast microscopy images with convolutional neural networks algorithm

Detalhes bibliográficos
Autor(a) principal: Ferreira, E. K. G. D.
Data de Publicação: 2023
Outros Autores: Lara, D. S. D., Silveira, Guilherme Ferreira
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Institucional da FIOCRUZ (ARCA)
Texto Completo: https://www.arca.fiocruz.br/handle/icict/57703
https://doi.org/10.1038/s41598-023-29694-7
Resumo: Fundação Oswaldo Cruz. Instituto Carlos Chagas. Curitiba, PR, Brasil.
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spelling Ferreira, E. K. G. D.Lara, D. S. D.Silveira, Guilherme Ferreira2023-04-06T18:11:43Z2023-04-06T18:11:43Z2023FEREIRA, E. K. G. D. et al. Cell quantification in digital contrast microscopy images with convolutional neural networks algorithm. Scientific Reports, v. 13, n. 2596, p. 1–11, 2023.2045-2322https://www.arca.fiocruz.br/handle/icict/57703https://doi.org/10.1038/s41598-023-29694-7porNature ResearchCell quantification in digital contrast microscopy images with convolutional neural networks algorithminfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleFundação Oswaldo Cruz. Instituto Carlos Chagas. Curitiba, PR, Brasil.Universidade Federal de Minas Gerais. Departamento de Engenharia Elétrica. Belo Horizonte, MG, Brasil.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Curitiba, PR, Brasil.High Content Screening (HCS) combines high throughput techniques with the ability to generate cellular images of biological systems. The objective of this work is to evaluate the performance of predictive models using CNN to identify the number of cells present in digital contrast microscopy images obtained by HCS. One way to evaluate the algorithm was through the Mean Squared Error metric. The MSE was 4,335.99 in the A549 cell line, 25,295.23 in the Huh7 and 36,897.03 in the 3T3. After obtaining these values, different parameters of the models were changed to verify how they behave. By reducing the number of images, the MSE increased considerably, with the A549 cell line changing to 49,973.52, Huh7 to 79,473.88 and 3T3 to 52,977.05. Correlation analyzes were performed for the diferent models. In lineage A549, the best model showed a positive correlation with R= 0.953. In Huh7, the best correlation of the model was R= 0.821, it was also a positive correlation. In 3T3, the models showed no correlation, with the best model having R= 0.100. The models performed well in quantifying the number of cells, and the number and quality of the images interfered with this predictive ability.Microscopy, ElectronModels, BiologicalMicroscopia EletrônicaModelos Biológicosinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-82991https://www.arca.fiocruz.br/bitstream/icict/57703/1/license.txt5a560609d32a3863062d77ff32785d58MD51ORIGINAL41598_2023_Article_29694.pdf41598_2023_Article_29694.pdfapplication/pdf2708076https://www.arca.fiocruz.br/bitstream/icict/57703/2/41598_2023_Article_29694.pdf44ae6b596088d8ce617c10f27976547bMD52icict/577032023-04-06 15:11:45.058oai:www.arca.fiocruz.br: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ório InstitucionalPUBhttps://www.arca.fiocruz.br/oai/requestrepositorio.arca@fiocruz.bropendoar:21352023-04-06T18:11:45Repositório Institucional da FIOCRUZ (ARCA) - Fundação Oswaldo Cruz (FIOCRUZ)false
dc.title.en_US.fl_str_mv Cell quantification in digital contrast microscopy images with convolutional neural networks algorithm
title Cell quantification in digital contrast microscopy images with convolutional neural networks algorithm
spellingShingle Cell quantification in digital contrast microscopy images with convolutional neural networks algorithm
Ferreira, E. K. G. D.
Microscopy, Electron
Models, Biological
Microscopia Eletrônica
Modelos Biológicos
title_short Cell quantification in digital contrast microscopy images with convolutional neural networks algorithm
title_full Cell quantification in digital contrast microscopy images with convolutional neural networks algorithm
title_fullStr Cell quantification in digital contrast microscopy images with convolutional neural networks algorithm
title_full_unstemmed Cell quantification in digital contrast microscopy images with convolutional neural networks algorithm
title_sort Cell quantification in digital contrast microscopy images with convolutional neural networks algorithm
author Ferreira, E. K. G. D.
author_facet Ferreira, E. K. G. D.
Lara, D. S. D.
Silveira, Guilherme Ferreira
author_role author
author2 Lara, D. S. D.
Silveira, Guilherme Ferreira
author2_role author
author
dc.contributor.author.fl_str_mv Ferreira, E. K. G. D.
Lara, D. S. D.
Silveira, Guilherme Ferreira
dc.subject.en.en_US.fl_str_mv Microscopy, Electron
Models, Biological
topic Microscopy, Electron
Models, Biological
Microscopia Eletrônica
Modelos Biológicos
dc.subject.decs.en_US.fl_str_mv Microscopia Eletrônica
Modelos Biológicos
description Fundação Oswaldo Cruz. Instituto Carlos Chagas. Curitiba, PR, Brasil.
publishDate 2023
dc.date.accessioned.fl_str_mv 2023-04-06T18:11:43Z
dc.date.available.fl_str_mv 2023-04-06T18:11:43Z
dc.date.issued.fl_str_mv 2023
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.citation.fl_str_mv FEREIRA, E. K. G. D. et al. Cell quantification in digital contrast microscopy images with convolutional neural networks algorithm. Scientific Reports, v. 13, n. 2596, p. 1–11, 2023.
dc.identifier.uri.fl_str_mv https://www.arca.fiocruz.br/handle/icict/57703
dc.identifier.issn.en_US.fl_str_mv 2045-2322
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1038/s41598-023-29694-7
identifier_str_mv FEREIRA, E. K. G. D. et al. Cell quantification in digital contrast microscopy images with convolutional neural networks algorithm. Scientific Reports, v. 13, n. 2596, p. 1–11, 2023.
2045-2322
url https://www.arca.fiocruz.br/handle/icict/57703
https://doi.org/10.1038/s41598-023-29694-7
dc.language.iso.fl_str_mv por
language por
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Nature Research
publisher.none.fl_str_mv Nature Research
dc.source.none.fl_str_mv reponame:Repositório Institucional da FIOCRUZ (ARCA)
instname:Fundação Oswaldo Cruz (FIOCRUZ)
instacron:FIOCRUZ
instname_str Fundação Oswaldo Cruz (FIOCRUZ)
instacron_str FIOCRUZ
institution FIOCRUZ
reponame_str Repositório Institucional da FIOCRUZ (ARCA)
collection Repositório Institucional da FIOCRUZ (ARCA)
bitstream.url.fl_str_mv https://www.arca.fiocruz.br/bitstream/icict/57703/1/license.txt
https://www.arca.fiocruz.br/bitstream/icict/57703/2/41598_2023_Article_29694.pdf
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repository.name.fl_str_mv Repositório Institucional da FIOCRUZ (ARCA) - Fundação Oswaldo Cruz (FIOCRUZ)
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