title: Atlas-based segmentation of brain tumor images using a Markov random field-based tumor growth model and non-rigid registration.
abstract: We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed with the aim of improving the registration accuracy in case of patients with space-occupying lesions. We perform tests on 2D axial slices of five different patient data sets and show that the approach gives good results for the segmentation of white matter, grey matter, cerebrospinal fluid and the tumor.
authors.affiliation: Institute of Surgical Technology and Biomechanics, University of Bern, Switzerland. stefan.bauer@istb.unibe.ch
authors.forename: Stefan
authors.initials: S
authors.lastname: Bauer
authors.forename: Christof
authors.initials: C
authors.lastname: Seiler
authors.forename: Thibaut
authors.initials: T
authors.lastname: Bardyn
authors.forename: Philippe
authors.initials: P
authors.lastname: Buechler
authors.forename: Mauricio
authors.initials: M
authors.lastname: Reyes
mesh_terms: D001271:Atlases as Topic; D006801:Humans; D008279:Magnetic Resonance Imaging; D008390:Markov Chains; D008954:Models, Biological; D009369:Neoplasms / Q000473:pathology*