Segmentation of brain structures in presence of a space-occupying lesion.

Détails

ID Serval
serval:BIB_1479E8B1A123
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Segmentation of brain structures in presence of a space-occupying lesion.
Périodique
Neuroimage
Auteur⸱e⸱s
Pollo C., Cuadra M.B., Cuisenaire O., Villemure J.G., Thiran J.P.
ISSN
1053-8119 (Print)
ISSN-L
1053-8119
Statut éditorial
Publié
Date de publication
2005
Volume
24
Numéro
4
Pages
990-996
Langue
anglais
Notes
Publication types: Journal ArticlePublication Status: ppublish
Résumé
Brain deformations induced by space-occupying lesions may result in unpredictable position and shape of functionally important brain structures. The aim of this study is to propose a method for segmentation of brain structures by deformation of a segmented brain atlas in presence of a space-occupying lesion. Our approach is based on an a priori model of lesion growth (MLG) that assumes radial expansion from a seeding point and involves three steps: first, an affine registration bringing the atlas and the patient into global correspondence; then, the seeding of a synthetic tumor into the brain atlas providing a template for the lesion; finally, the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. The method was applied on two meningiomas inducing a pure displacement of the underlying brain structures, and segmentation accuracy of ventricles and basal ganglia was assessed. Results show that the segmented structures were consistent with the patient's anatomy and that the deformation accuracy of surrounding brain structures was highly dependent on the accurate placement of the tumor seeding point. Further improvements of the method will optimize the segmentation accuracy. Visualization of brain structures provides useful information for therapeutic consideration of space-occupying lesions, including surgical, radiosurgical, and radiotherapeutic planning, in order to increase treatment efficiency and prevent neurological damage.
Mots-clé
Algorithms, Brain/pathology, Disease Progression, Elasticity, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging/statistics & numerical data, Meningioma/pathology, Models, Anatomic
Pubmed
Web of science
Création de la notice
24/02/2012 15:27
Dernière modification de la notice
20/08/2019 13:43
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