GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices

Autores
Isa Jara, Ramiro Fernando; Pérez Sosa, Camilo José; Macote Yparraguirre, Erick Leonel; Revollo Sarmiento, Natalia Veronica; Lerner, Betiana; Miriuka, Santiago Gabriel; Delrieux, Claudio Augusto; Pérez, Maximiliano; Mertelsmann, Roland
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments.
Fil: Isa Jara, Ramiro Fernando. Escuela Superior Politécnica de Chimborazo; Ecuador. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Pérez Sosa, Camilo José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; Argentina
Fil: Macote Yparraguirre, Erick Leonel. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Revollo Sarmiento, Natalia Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Fil: Lerner, Betiana. Florida International University; Estados Unidos. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Miriuka, Santiago Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina
Fil: Delrieux, Claudio Augusto. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Pérez, Maximiliano. Florida International University; Estados Unidos. Universidad de Buenos Aires; Argentina
Fil: Mertelsmann, Roland. Albert Ludwigs University of Freiburg; Alemania
Materia
APOPTOSIS PROCESS
BIOLOGICAL IMAGE SEGMENTATION
LINEAR REGRESSION
REAL-TIME ANALYSIS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/205127
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic DevicesIsa Jara, Ramiro FernandoPérez Sosa, Camilo JoséMacote Yparraguirre, Erick LeonelRevollo Sarmiento, Natalia VeronicaLerner, BetianaMiriuka, Santiago GabrielDelrieux, Claudio AugustoPérez, MaximilianoMertelsmann, RolandAPOPTOSIS PROCESSBIOLOGICAL IMAGE SEGMENTATIONLINEAR REGRESSIONREAL-TIME ANALYSIShttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments.Fil: Isa Jara, Ramiro Fernando. Escuela Superior Politécnica de Chimborazo; Ecuador. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pérez Sosa, Camilo José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; ArgentinaFil: Macote Yparraguirre, Erick Leonel. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Revollo Sarmiento, Natalia Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Lerner, Betiana. Florida International University; Estados Unidos. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Miriuka, Santiago Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Delrieux, Claudio Augusto. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pérez, Maximiliano. Florida International University; Estados Unidos. Universidad de Buenos Aires; ArgentinaFil: Mertelsmann, Roland. Albert Ludwigs University of Freiburg; AlemaniaI S & T - Soc Imaging Science Technology2022-10-14info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/205127Isa Jara, Ramiro Fernando; Pérez Sosa, Camilo José; Macote Yparraguirre, Erick Leonel; Revollo Sarmiento, Natalia Veronica; Lerner, Betiana; et al.; GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices; I S & T - Soc Imaging Science Technology; Journal Of Imaging Science And Technology; 8; 10; 14-10-2022; 1-181062-3701CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3390/jimaging8100281info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2313-433X/8/10/281info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-26T09:10:41Zoai:ri.conicet.gov.ar:11336/205127instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-11-26 09:10:41.568CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices
title GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices
spellingShingle GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices
Isa Jara, Ramiro Fernando
APOPTOSIS PROCESS
BIOLOGICAL IMAGE SEGMENTATION
LINEAR REGRESSION
REAL-TIME ANALYSIS
title_short GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices
title_full GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices
title_fullStr GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices
title_full_unstemmed GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices
title_sort GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices
dc.creator.none.fl_str_mv Isa Jara, Ramiro Fernando
Pérez Sosa, Camilo José
Macote Yparraguirre, Erick Leonel
Revollo Sarmiento, Natalia Veronica
Lerner, Betiana
Miriuka, Santiago Gabriel
Delrieux, Claudio Augusto
Pérez, Maximiliano
Mertelsmann, Roland
author Isa Jara, Ramiro Fernando
author_facet Isa Jara, Ramiro Fernando
Pérez Sosa, Camilo José
Macote Yparraguirre, Erick Leonel
Revollo Sarmiento, Natalia Veronica
Lerner, Betiana
Miriuka, Santiago Gabriel
Delrieux, Claudio Augusto
Pérez, Maximiliano
Mertelsmann, Roland
author_role author
author2 Pérez Sosa, Camilo José
Macote Yparraguirre, Erick Leonel
Revollo Sarmiento, Natalia Veronica
Lerner, Betiana
Miriuka, Santiago Gabriel
Delrieux, Claudio Augusto
Pérez, Maximiliano
Mertelsmann, Roland
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv APOPTOSIS PROCESS
BIOLOGICAL IMAGE SEGMENTATION
LINEAR REGRESSION
REAL-TIME ANALYSIS
topic APOPTOSIS PROCESS
BIOLOGICAL IMAGE SEGMENTATION
LINEAR REGRESSION
REAL-TIME ANALYSIS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments.
Fil: Isa Jara, Ramiro Fernando. Escuela Superior Politécnica de Chimborazo; Ecuador. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Pérez Sosa, Camilo José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; Argentina
Fil: Macote Yparraguirre, Erick Leonel. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Revollo Sarmiento, Natalia Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Fil: Lerner, Betiana. Florida International University; Estados Unidos. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Miriuka, Santiago Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina
Fil: Delrieux, Claudio Augusto. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Pérez, Maximiliano. Florida International University; Estados Unidos. Universidad de Buenos Aires; Argentina
Fil: Mertelsmann, Roland. Albert Ludwigs University of Freiburg; Alemania
description Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments.
publishDate 2022
dc.date.none.fl_str_mv 2022-10-14
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/205127
Isa Jara, Ramiro Fernando; Pérez Sosa, Camilo José; Macote Yparraguirre, Erick Leonel; Revollo Sarmiento, Natalia Veronica; Lerner, Betiana; et al.; GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices; I S & T - Soc Imaging Science Technology; Journal Of Imaging Science And Technology; 8; 10; 14-10-2022; 1-18
1062-3701
CONICET Digital
CONICET
url http://hdl.handle.net/11336/205127
identifier_str_mv Isa Jara, Ramiro Fernando; Pérez Sosa, Camilo José; Macote Yparraguirre, Erick Leonel; Revollo Sarmiento, Natalia Veronica; Lerner, Betiana; et al.; GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices; I S & T - Soc Imaging Science Technology; Journal Of Imaging Science And Technology; 8; 10; 14-10-2022; 1-18
1062-3701
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3390/jimaging8100281
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2313-433X/8/10/281
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv I S & T - Soc Imaging Science Technology
publisher.none.fl_str_mv I S & T - Soc Imaging Science Technology
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.011256

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