Model-based Segmentation and Image Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

Détails

ID Serval
serval:BIB_C45D09803C83
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Titre
Model-based Segmentation and Image Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning
Titre de la conférence
VIPIMAGE 2011 - ECCOMAS 2nd Thematic Conference on Computational Vision and Medical Image Processing
Auteur(s)
Bach C.M.Gorthi Subrahmanyam , Karahanoglu F.I., Salvador F., Pica A., Do H.P., Balmer A., Munier F., Thiran J.P.
Adresse
Porto, Portugal, October 14-16, 2009
Statut éditorial
Publié
Date de publication
2009
Langue
anglais
Résumé
For radiotherapy treatment planning of retinoblastoma inchildhood, Computed Tomography (CT) represents thestandard method for tumor volume delineation, despitesome inherent limitations. CT scan is very useful inproviding information on physical density for dosecalculation and morphological volumetric information butpresents a low sensitivity in assessing the tumorviability. On the other hand, 3D ultrasound (US) allows ahigh accurate definition of the tumor volume thanks toits high spatial resolution but it is not currentlyintegrated in the treatment planning but used only fordiagnosis and follow-up. Our ultimate goal is anautomatic segmentation of gross tumor volume (GTV) in the3D US, the segmentation of the organs at risk (OAR) inthe CT and the registration of both. In this paper, wepresent some preliminary results in this direction. Wepresent 3D active contour-based segmentation of the eyeball and the lens in CT images; the presented approachincorporates the prior knowledge of the anatomy by usinga 3D geometrical eye model. The automated segmentationresults are validated by comparing with manualsegmentations. Then, for the fusion of 3D CT and USimages, we present two approaches: (i) landmark-basedtransformation, and (ii) object-based transformation thatmakes use of eye ball contour information on CT and USimages.
Mots-clé
LTS5, Segmentation, Active contours, Multimodal imaging, 3D Ultrasound imaging, Radiotherapy planning, Radiotherapy
Création de la notice
29/11/2011 16:40
Dernière modification de la notice
20/08/2019 15:39
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