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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/123790
Ver los metadatos del registro completo
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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 |
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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 |
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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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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application/pdf 37-48 |
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