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

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dc.contributor.author Isa-Jara, Ramiro
dc.contributor.author Pérez-Sosa, Camilo
dc.contributor.author Macote-Yparraguirre, Erick
dc.contributor.author Revollo, Natalia
dc.contributor.author Lerner, Betiana
dc.contributor.author Miriuka, Santiago Gabriel
dc.contributor.author Delrieux, Claudio
dc.contributor.author Pérez, Maximiliano S.
dc.contributor.author Mertelsmann, Roland
dc.date.accessioned 2022-10-18T14:52:48Z
dc.date.available 2022-10-18T14:52:48Z
dc.date.issued 2022-10-14
dc.identifier.citation Isa-Jara R, Pérez-Sosa C, Macote-Yparraguirre E, Revollo N, Lerner B, Miriuka S, Delrieux C, Pérez MS, Mertelsmann R. GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices. J. Imaging 2022, 8(10), 281. es_ES
dc.identifier.uri https://repositorio.fleni.org.ar/xmlui/handle/123456789/693
dc.identifier.uri https://www.mdpi.com/2313-433X/8/10/281
dc.description.abstract 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. es_ES
dc.language.iso eng es_ES
dc.publisher MDPI es_ES
dc.rights info:eu-repo/semantics/openAccess
dc.rights.uri https://creativecommons.org/licenses/by/2.5/ar/
dc.subject Modelos Lineales es_ES
dc.subject Linear Models es_ES
dc.subject Apoptosis es_ES
dc.title GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices es_ES
dc.type info:eu-repo/semantics/article es_ES
dc.type info:eu-repo/semantics/publishedVersion
dc.description.fil Fil: Miriuka, Santiago Gabriel. FLENI-CONICET. Laboratorio de Investigación Aplicada a las Neurociencias; Argentina.
dc.description.fil Fil: Isa-Jara, Ramiro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Faculty of Informatics and Electronic; Ecuador.
dc.description.fil Fil: Pérez-Sosa, Camilo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; Argentina.
dc.description.fil Fil: Macote-Yparraguirre, Erick. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; Argentina.
dc.description.fil Fil: Revollo, Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; Argentina.
dc.description.fil Fil: Lerner, Betiana. Florida International University. Department of Electrical and Computer Engineering; Estados Unidos. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Collaborative Research Institute Intelligent Oncology; Alemania.
dc.description.fil Fil: Delrieux, Claudio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; Argentina.
dc.description.fil Fil: Pérez, Maximiliano S. Florida International University. Department of Electrical and Computer Engineering; Estados Unidos. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Collaborative Research Institute Intelligent Oncology; Alemania.
dc.description.fil Fil: Mertelsmann, Roland. University of Freiburg. Faculty of Medicine. Medical Center. Department of Medicine I; Alemania.
dc.relation.ispartofVOLUME 8
dc.relation.ispartofNUMBER 10
dc.relation.ispartofPAGINATION 281.
dc.relation.ispartofTITLE Journal of Imaging
dc.relation.ispartofISSN 2313-433X
dc.type.snrd info:ar-repo/semantics/artículo es_ES


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