Atlas-based segmentation of pathological MR brain images using a model of lesion growth.
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.
Cuadra
Meritxell Bach
MB
Signal Processing Institute, Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland. Meritxell.Bach@epfl.ch
Pollo
Claudio
C
Bardera
Anton
A
Cuisenaire
Olivier
O
Villemure
Jean-Guy
JG
Thiran
Jean-Philippe
JP
D000465:Algorithms; D000716:Anatomy, Artistic / Q000379:methods; D001185:Artificial Intelligence; D001932:Brain Neoplasms / Q000175:diagnosis*; D016000:Cluster Analysis; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008488:Medical Illustration; D008577:Meningeal Neoplasms / Q000175:diagnosis; D008579:Meningioma / Q000175:diagnosis*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique