title: A generative model for brain tumor segmentation in multi-modal images.
abstract: We introduce a generative probabilistic model for segmentation of tumors in multi-dimensional images. The model allows for different tumor boundaries in each channel, reflecting difference in tumor appearance across modalities. We augment a probabilistic atlas of healthy tissue priors with a latent atlas of the lesion and derive the estimation algorithm to extract tumor boundaries and the latent atlas from the image data. We present experiments on 25 glioma patient data sets, demonstrating significant improvement over the traditional multivariate tumor segmentation.
authors.affiliation: 1 Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA.
authors.forename: Bjoern H
authors.initials: BH
authors.lastname: Menze
authors.forename: Koen
authors.initials: K
authors.lastname: Van Leemput
authors.forename: Danial
authors.initials: D
authors.lastname: Lashkari
authors.forename: Marc-André
authors.initials: MA
authors.lastname: Weber
authors.forename: Nicholas
authors.initials: N
authors.lastname: Ayache
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000175:diagnosis*; D003198:Computer Simulation; D005910:Glioma / Q000175:diagnosis*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique