title: Spatial decision forests for MS lesion segmentation in multi-channel MR images.
abstract: A new algorithm is presented for the automatic segmentation of Multiple Sclerosis (MS) lesions in 3D MR images. It builds on the discriminative random decision forest framework to provide a voxel-wise probabilistic classification of the volume. Our method uses multi-channel MIR intensities (T1, T2, Flair), spatial prior and long-range comparisons with 3D regions to discriminate lesions. A symmetry feature is introduced accounting for the fact that some MS lesions tend to develop in an asymmetric way. Quantitative evaluation of the data is carried out on publicly available labeled cases from the MS Lesion Segmentation Challenge 2008 dataset and demonstrates improved results over the state of the art.
authors.affiliation: Asclepios Research Project, INRIA Sophia-Antipolis, France.
authors.forename: Ezequiel
authors.initials: E
authors.lastname: Geremia
authors.forename: Bjoern H
authors.initials: BH
authors.lastname: Menze
authors.forename: Olivier
authors.initials: O
authors.lastname: Clatz
authors.forename: Ender
authors.initials: E
authors.lastname: Konukoglu
authors.forename: Antonio
authors.initials: A
authors.lastname: Criminisi
authors.forename: Nicholas
authors.initials: N
authors.lastname: Ayache
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D003661:Decision Support Techniques; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity