title: Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images.
abstract: A new algorithm is presented for the automatic segmentation of Multiple Sclerosis (MS) lesions in 3D Magnetic Resonance (MR) images. It builds on a discriminative random decision forest framework to provide a voxel-wise probabilistic classification of the volume. The method uses multi-channel MR intensities (T1, T2, and FLAIR), knowledge on tissue classes and long-range spatial context to discriminate lesions from background. A symmetry feature is introduced accounting for the fact that some MS lesions tend to develop in an asymmetric way. Quantitative evaluation of the proposed methods is carried out on publicly available labeled cases from the MICCAI MS Lesion Segmentation Challenge 2008 dataset. When tested on the same data, the presented method compares favorably to all earlier methods. In an a posteriori analysis, we show how selected features during classification can be ranked according to their discriminative power and reveal the most important ones.
authors.affiliation: Asclepios Research Project, INRIA Sophia-Antipolis, France. Ezequiel.Geremia@inria.fr
authors.forename: Ezequiel
authors.initials: E
authors.lastname: Geremia
authors.forename: Olivier
authors.initials: O
authors.lastname: Clatz
authors.forename: Bjoern H
authors.initials: BH
authors.lastname: Menze
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; D001931:Brain Mapping / Q000379:methods*; D003663:Decision Trees; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D009103:Multiple Sclerosis / Q000473:pathology*