Segmentation of 3D vasculatures for interventional radiology simulation.
Stud Health Technol Inform
; 163: 599-605, 2011.
Article
en En
| MEDLINE
| ID: mdl-21335864
ABSTRACT
Training in interventional radiology is slowly shifting towards simulation which allows the repetition of many interventions without putting the patient at risk. Accurate segmentation of anatomical structures is a prerequisite of realistic surgical simulation. Therefore, our aim is to develop a generic approach to provide fast and precise segmentation of various virtual anatomies covering a wide range of pathology, directly from patient CT/MRA images. This paper presents a segmentation framework including two segmentation methods:
region model based level set segmentation and hierarchical segmentation. We compare them to an open source application ITK-SNAP which provides similar approaches. The subjective human influence such as inconsistent inter-observer errors and aliasing artifacts etc. are analysed. The proposed segmentation techniques have been successfully applied to create a database of various anatomies with different pathologies, which is used in computer-based simulation for interventional radiology training.
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Base de datos:
MEDLINE
Asunto principal:
Vasos Sanguíneos
/
Reconocimiento de Normas Patrones Automatizadas
/
Angiografía
/
Radiografía Intervencional
/
Imagenología Tridimensional
/
Modelos Anatómicos
/
Modelos Cardiovasculares
Idioma:
En
Revista:
Stud Health Technol Inform
Asunto de la revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Año:
2011
Tipo del documento:
Article