Segmentation of brain structures in presence of a space-occupying lesion.
Details
Serval ID
serval:BIB_1479E8B1A123
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Segmentation of brain structures in presence of a space-occupying lesion.
Journal
Neuroimage
ISSN
1053-8119 (Print)
ISSN-L
1053-8119
Publication state
Published
Issued date
2005
Volume
24
Number
4
Pages
990-996
Language
english
Notes
Publication types: Journal ArticlePublication Status: ppublish
Abstract
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.
Keywords
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
Create date
24/02/2012 14:27
Last modification date
20/08/2019 12:43