Volumetric Segmentation of Pelvic Organs from MRI Acquisitions

Autores
Namías, Rafael; Fresno, Mariana del; D’Amato, J. P.; Bellemare, M. E.; Vénere, Marcelo
Año de publicación
2012
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this work we present part of a Pelvis Dynamics Modeling System for pre-surgical assistance in the pelvic organ prolapse disease. In this condition, the most common affected organs are the uterus, the bladder and the rectum. The Magnetic Resonance Imaging (MRI) is the gold standard non-invasive imaging technique to evaluate this condition. The MRI’s acquisitions provide spatial information that is essential to build tri-dimensional (3D) models and run physical simulations that recreate the prolapse. In these acquisitions, the above mentioned organs, present blurred borders and different textures. Therefore, its extraction in not trivial at all. We pose an hybrid semi-automatic segmentation strategy which combines Region Growing (RG) and Active Surfaces in MRI scans to retrieve surface meshes of the organs of interest. We show some real cases, one applying the complete process in detail and the others, providing final results attained by the method which shows high quality segmentations achieved with a low computational cost.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Medical Imaging
Segmentation
Active Surfaces
Magnetic Resonance
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/123790

id SEDICI_8b86a4e56c7df95a0a63c5a890a88360
oai_identifier_str oai:sedici.unlp.edu.ar:10915/123790
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Volumetric Segmentation of Pelvic Organs from MRI AcquisitionsNamías, RafaelFresno, Mariana delD’Amato, J. P.Bellemare, M. E.Vénere, MarceloCiencias InformáticasMedical ImagingSegmentationActive SurfacesMagnetic ResonanceIn this work we present part of a Pelvis Dynamics Modeling System for pre-surgical assistance in the pelvic organ prolapse disease. In this condition, the most common affected organs are the uterus, the bladder and the rectum. The Magnetic Resonance Imaging (MRI) is the gold standard non-invasive imaging technique to evaluate this condition. The MRI’s acquisitions provide spatial information that is essential to build tri-dimensional (3D) models and run physical simulations that recreate the prolapse. In these acquisitions, the above mentioned organs, present blurred borders and different textures. Therefore, its extraction in not trivial at all. We pose an hybrid semi-automatic segmentation strategy which combines Region Growing (RG) and Active Surfaces in MRI scans to retrieve surface meshes of the organs of interest. We show some real cases, one applying the complete process in detail and the others, providing final results attained by the method which shows high quality segmentations achieved with a low computational cost.Sociedad Argentina de Informática e Investigación Operativa2012-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf37-48http://sedici.unlp.edu.ar/handle/10915/123790enginfo:eu-repo/semantics/altIdentifier/url/https://41jaiio.sadio.org.ar/sites/default/files/4_AST_2012.pdfinfo:eu-repo/semantics/altIdentifier/issn/1850-2806info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:29:39Zoai:sedici.unlp.edu.ar:10915/123790Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:29:39.64SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Volumetric Segmentation of Pelvic Organs from MRI Acquisitions
title Volumetric Segmentation of Pelvic Organs from MRI Acquisitions
spellingShingle Volumetric Segmentation of Pelvic Organs from MRI Acquisitions
Namías, Rafael
Ciencias Informáticas
Medical Imaging
Segmentation
Active Surfaces
Magnetic Resonance
title_short Volumetric Segmentation of Pelvic Organs from MRI Acquisitions
title_full Volumetric Segmentation of Pelvic Organs from MRI Acquisitions
title_fullStr Volumetric Segmentation of Pelvic Organs from MRI Acquisitions
title_full_unstemmed Volumetric Segmentation of Pelvic Organs from MRI Acquisitions
title_sort Volumetric Segmentation of Pelvic Organs from MRI Acquisitions
dc.creator.none.fl_str_mv Namías, Rafael
Fresno, Mariana del
D’Amato, J. P.
Bellemare, M. E.
Vénere, Marcelo
author Namías, Rafael
author_facet Namías, Rafael
Fresno, Mariana del
D’Amato, J. P.
Bellemare, M. E.
Vénere, Marcelo
author_role author
author2 Fresno, Mariana del
D’Amato, J. P.
Bellemare, M. E.
Vénere, Marcelo
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Medical Imaging
Segmentation
Active Surfaces
Magnetic Resonance
topic Ciencias Informáticas
Medical Imaging
Segmentation
Active Surfaces
Magnetic Resonance
dc.description.none.fl_txt_mv In this work we present part of a Pelvis Dynamics Modeling System for pre-surgical assistance in the pelvic organ prolapse disease. In this condition, the most common affected organs are the uterus, the bladder and the rectum. The Magnetic Resonance Imaging (MRI) is the gold standard non-invasive imaging technique to evaluate this condition. The MRI’s acquisitions provide spatial information that is essential to build tri-dimensional (3D) models and run physical simulations that recreate the prolapse. In these acquisitions, the above mentioned organs, present blurred borders and different textures. Therefore, its extraction in not trivial at all. We pose an hybrid semi-automatic segmentation strategy which combines Region Growing (RG) and Active Surfaces in MRI scans to retrieve surface meshes of the organs of interest. We show some real cases, one applying the complete process in detail and the others, providing final results attained by the method which shows high quality segmentations achieved with a low computational cost.
Sociedad Argentina de Informática e Investigación Operativa
description In this work we present part of a Pelvis Dynamics Modeling System for pre-surgical assistance in the pelvic organ prolapse disease. In this condition, the most common affected organs are the uterus, the bladder and the rectum. The Magnetic Resonance Imaging (MRI) is the gold standard non-invasive imaging technique to evaluate this condition. The MRI’s acquisitions provide spatial information that is essential to build tri-dimensional (3D) models and run physical simulations that recreate the prolapse. In these acquisitions, the above mentioned organs, present blurred borders and different textures. Therefore, its extraction in not trivial at all. We pose an hybrid semi-automatic segmentation strategy which combines Region Growing (RG) and Active Surfaces in MRI scans to retrieve surface meshes of the organs of interest. We show some real cases, one applying the complete process in detail and the others, providing final results attained by the method which shows high quality segmentations achieved with a low computational cost.
publishDate 2012
dc.date.none.fl_str_mv 2012-08
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/123790
url http://sedici.unlp.edu.ar/handle/10915/123790
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://41jaiio.sadio.org.ar/sites/default/files/4_AST_2012.pdf
info:eu-repo/semantics/altIdentifier/issn/1850-2806
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
37-48
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1844616175858745344
score 13.069144