Fast, accurate, robust and Open Source Brain Extraction Tool (OSBET)

Autores
Namias, Rafael; Donnelly Kehoe, Patricio Andres; D'amato, Juan Pablo; Nagel, J.
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The removal of non-brain regions in neuroimaging is a critical task to perform a favorable preprocessing. The skull-stripping depends on different factors including the noise level in the image, the anatomy of the subject being scanned and the acquisition sequence. For these and other reasons, an ideal brain extraction method should be fast, accurate, user friendly, open-source and knowledge based (to allow for the interaction with the algorithm in case the expected outcome is not being obtained), producing stable results and making it possible to automate the process for large datasets. There are already a large number of validated tools to perform this task but none of them meets the desired characteristics. In this paper we introduced an open source brain extraction tool (OSBET), composed of four steps using simple well-known operations such as: optimal thresholding, binary morphology, labeling and geometrical analysis, that aims to assemble all the desired features. We present an experiment comparing OSBET with other six state-of-the-art techniques against a publicly available dataset consisting of 40 T1-weighted 3D scans and their corresponding manually segmented images. OSBET gave both: a short duration with an excellent accuracy, getting the best Dice Coefficient metric. Further validation should be performed, for instance, in unhealthy population, to generalize its usage for clinical purposes.
Fil: Namias, Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
Fil: Donnelly Kehoe, Patricio Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
Fil: D'amato, Juan Pablo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados; Argentina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Nagel, J.. Instituto Gamma; Argentina
Materia
Skullstripping
Magnetic Resonance Imaging
Neuroscience
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:11336/15241
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Fast, accurate, robust and Open Source Brain Extraction Tool (OSBET)Namias, RafaelDonnelly Kehoe, Patricio AndresD'amato, Juan PabloNagel, J.SkullstrippingMagnetic Resonance ImagingNeurosciencehttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1https://purl.org/becyt/ford/2.6https://purl.org/becyt/ford/2The removal of non-brain regions in neuroimaging is a critical task to perform a favorable preprocessing. The skull-stripping depends on different factors including the noise level in the image, the anatomy of the subject being scanned and the acquisition sequence. For these and other reasons, an ideal brain extraction method should be fast, accurate, user friendly, open-source and knowledge based (to allow for the interaction with the algorithm in case the expected outcome is not being obtained), producing stable results and making it possible to automate the process for large datasets. There are already a large number of validated tools to perform this task but none of them meets the desired characteristics. In this paper we introduced an open source brain extraction tool (OSBET), composed of four steps using simple well-known operations such as: optimal thresholding, binary morphology, labeling and geometrical analysis, that aims to assemble all the desired features. We present an experiment comparing OSBET with other six state-of-the-art techniques against a publicly available dataset consisting of 40 T1-weighted 3D scans and their corresponding manually segmented images. OSBET gave both: a short duration with an excellent accuracy, getting the best Dice Coefficient metric. Further validation should be performed, for instance, in unhealthy population, to generalize its usage for clinical purposes.Fil: Namias, Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; ArgentinaFil: Donnelly Kehoe, Patricio Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; ArgentinaFil: D'amato, Juan Pablo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados; Argentina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Nagel, J.. Instituto Gamma; ArgentinaSpie2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/15241Namias, Rafael; Donnelly Kehoe, Patricio Andres; D'amato, Juan Pablo; Nagel, J.; Fast, accurate, robust and Open Source Brain Extraction Tool (OSBET); Spie; Spie; 9681; -1-2015; 1-110277-786Xenginfo:eu-repo/semantics/altIdentifier/doi/10.1117/12.2207834info:eu-repo/semantics/altIdentifier/url/http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2479338info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:44:14Zoai:ri.conicet.gov.ar:11336/15241instacron: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:34982025-09-03 09:44:15.229CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Fast, accurate, robust and Open Source Brain Extraction Tool (OSBET)
title Fast, accurate, robust and Open Source Brain Extraction Tool (OSBET)
spellingShingle Fast, accurate, robust and Open Source Brain Extraction Tool (OSBET)
Namias, Rafael
Skullstripping
Magnetic Resonance Imaging
Neuroscience
title_short Fast, accurate, robust and Open Source Brain Extraction Tool (OSBET)
title_full Fast, accurate, robust and Open Source Brain Extraction Tool (OSBET)
title_fullStr Fast, accurate, robust and Open Source Brain Extraction Tool (OSBET)
title_full_unstemmed Fast, accurate, robust and Open Source Brain Extraction Tool (OSBET)
title_sort Fast, accurate, robust and Open Source Brain Extraction Tool (OSBET)
dc.creator.none.fl_str_mv Namias, Rafael
Donnelly Kehoe, Patricio Andres
D'amato, Juan Pablo
Nagel, J.
author Namias, Rafael
author_facet Namias, Rafael
Donnelly Kehoe, Patricio Andres
D'amato, Juan Pablo
Nagel, J.
author_role author
author2 Donnelly Kehoe, Patricio Andres
D'amato, Juan Pablo
Nagel, J.
author2_role author
author
author
dc.subject.none.fl_str_mv Skullstripping
Magnetic Resonance Imaging
Neuroscience
topic Skullstripping
Magnetic Resonance Imaging
Neuroscience
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/2.6
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The removal of non-brain regions in neuroimaging is a critical task to perform a favorable preprocessing. The skull-stripping depends on different factors including the noise level in the image, the anatomy of the subject being scanned and the acquisition sequence. For these and other reasons, an ideal brain extraction method should be fast, accurate, user friendly, open-source and knowledge based (to allow for the interaction with the algorithm in case the expected outcome is not being obtained), producing stable results and making it possible to automate the process for large datasets. There are already a large number of validated tools to perform this task but none of them meets the desired characteristics. In this paper we introduced an open source brain extraction tool (OSBET), composed of four steps using simple well-known operations such as: optimal thresholding, binary morphology, labeling and geometrical analysis, that aims to assemble all the desired features. We present an experiment comparing OSBET with other six state-of-the-art techniques against a publicly available dataset consisting of 40 T1-weighted 3D scans and their corresponding manually segmented images. OSBET gave both: a short duration with an excellent accuracy, getting the best Dice Coefficient metric. Further validation should be performed, for instance, in unhealthy population, to generalize its usage for clinical purposes.
Fil: Namias, Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
Fil: Donnelly Kehoe, Patricio Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
Fil: D'amato, Juan Pablo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados; Argentina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Nagel, J.. Instituto Gamma; Argentina
description The removal of non-brain regions in neuroimaging is a critical task to perform a favorable preprocessing. The skull-stripping depends on different factors including the noise level in the image, the anatomy of the subject being scanned and the acquisition sequence. For these and other reasons, an ideal brain extraction method should be fast, accurate, user friendly, open-source and knowledge based (to allow for the interaction with the algorithm in case the expected outcome is not being obtained), producing stable results and making it possible to automate the process for large datasets. There are already a large number of validated tools to perform this task but none of them meets the desired characteristics. In this paper we introduced an open source brain extraction tool (OSBET), composed of four steps using simple well-known operations such as: optimal thresholding, binary morphology, labeling and geometrical analysis, that aims to assemble all the desired features. We present an experiment comparing OSBET with other six state-of-the-art techniques against a publicly available dataset consisting of 40 T1-weighted 3D scans and their corresponding manually segmented images. OSBET gave both: a short duration with an excellent accuracy, getting the best Dice Coefficient metric. Further validation should be performed, for instance, in unhealthy population, to generalize its usage for clinical purposes.
publishDate 2015
dc.date.none.fl_str_mv 2015
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/11336/15241
Namias, Rafael; Donnelly Kehoe, Patricio Andres; D'amato, Juan Pablo; Nagel, J.; Fast, accurate, robust and Open Source Brain Extraction Tool (OSBET); Spie; Spie; 9681; -1-2015; 1-11
0277-786X
url http://hdl.handle.net/11336/15241
identifier_str_mv Namias, Rafael; Donnelly Kehoe, Patricio Andres; D'amato, Juan Pablo; Nagel, J.; Fast, accurate, robust and Open Source Brain Extraction Tool (OSBET); Spie; Spie; 9681; -1-2015; 1-11
0277-786X
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1117/12.2207834
info:eu-repo/semantics/altIdentifier/url/http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2479338
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
dc.publisher.none.fl_str_mv Spie
publisher.none.fl_str_mv Spie
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 13.13397

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