Atlas-based segmentation of pathological MR brain images using a model of lesion growth.

Détails

ID Serval
serval:BIB_806EC2D7D712
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Atlas-based segmentation of pathological MR brain images using a model of lesion growth.
Périodique
Ieee Transactions On Medical Imaging
Auteur⸱e⸱s
Cuadra M.B., Pollo C., Bardera A., Cuisenaire O., Villemure J.G., Thiran J.P.
ISSN
0278-0062 (Print)
ISSN-L
0278-0062
Statut éditorial
Publié
Date de publication
2004
Volume
23
Numéro
10
Pages
1301-1314
Langue
anglais
Notes
Publication types: Comparative Study ; Evaluation Studies ; Journal Article ; Research Support, Non-U.S. Gov't ; Validation StudiesPublication Status: ppublish
Résumé
We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.
Mots-clé
Algorithms, Anatomy, Artistic/methods, Artificial Intelligence, Brain Neoplasms/diagnosis, Cluster Analysis, Computer Simulation, Humans, Image Enhancement/methods, Image Interpretation, Computer-Assisted/methods, Imaging, Three-Dimensional/methods, Information Storage and Retrieval/methods, Magnetic Resonance Imaging/methods, Medical Illustration, Meningeal Neoplasms/diagnosis, Meningioma/diagnosis, Models, Biological, Models, Statistical, Pattern Recognition, Automated/methods, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique
Pubmed
Web of science
Création de la notice
24/02/2012 14:27
Dernière modification de la notice
20/08/2019 14:40
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