Segmentation of brain structures in presence of a space-occupying lesion.
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.
Pollo
Claudio
C
Department of Neurosurgery, Centre Hospitalier Universitaire Vaudois (CHUV), CH-1011 Lausanne, Switzerland. claudio.pollo@chuv.hospvd.ch
Cuadra
Meritxell Bach
MB
Cuisenaire
Olivier
O
Villemure
Jean-Guy
JG
Thiran
Jean-Philippe
JP
D000465:Algorithms; D001921:Brain / Q000473:pathology*; D018450:Disease Progression; D004548:Elasticity; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data; D008579:Meningioma / Q000473:pathology*; D008953:Models, Anatomic