title: Joint tumor segmentation and dense deformable registration of brain MR images.
abstract: In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of tumors as well as a segmentation through a registration term aiming the separation between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.
authors.affiliation: Center for Visual Computing, Ecole Centrale Paris, Chatenay Malabry, France.
authors.forename: Sarah
authors.initials: S
authors.lastname: Parisot
authors.forename: Hugues
authors.initials: H
authors.lastname: Duffau
authors.forename: Stéphane
authors.initials: S
authors.lastname: Chemouny
authors.forename: Nikos
authors.initials: N
authors.lastname: Paragios
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001932:Brain Neoplasms / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique