title: Joint segmentation and deformable registration of brain scans guided by a tumor growth model.
abstract: This paper presents an approach for joint segmentation and deformable registration of brain scans of glioma patients to a normal atlas. The proposed method is based on the Expectation Maximization (EM) algorithm that incorporates a glioma growth model for atlas seeding, a process which modifies the normal atlas into one with a tumor and edema. The modified atlas is registered into the patient space and utilized for the posterior probability estimation of various tissue labels. EM iteratively refines the estimates of the registration parameters, the posterior probabilities of tissue labels and the tumor growth model parameters. We have applied this approach to 10 glioma scans acquired with four Magnetic Resonance (MR) modalities (T1, T1-CE, T2 and FLAIR) and validated the result by comparing them to manual segmentations by clinical experts. The resulting segmentations look promising and quantitatively match well with the expert provided ground truth.
authors: Section for Biomedical Image Analysis, Suite 380, 3600 Market St.,19104 Philadelphia, USA. ali.gooya@uphs.upenn.edu
authors: Ali
authors: A
authors: Gooya
authors: Kilian M
authors: KM
authors: Pohl
authors: Michel
authors: M
authors: Bilello
authors: George
authors: G
authors: Biros
authors: Christos
authors: C
authors: Davatzikos
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology; D001931:Brain Mapping / Q000379:methods*; D001932:Brain Neoplasms / Q000473:pathology*; D005910:Glioma / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D009369:Neoplasms / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods; D011336:Probability