title: Discriminative, semantic segmentation of brain tissue in MR images.
abstract: A new algorithm is presented for the automatic segmentation and classification of brain tissue from 3D MR scans. It uses discriminative Random Decision Forest classification and takes into account partial volume effects. This is combined with correction of intensities for the MR bias field, in conjunction with a learned model of spatial context, to achieve accurate voxel-wise classification. Our quantitative validation, carried out on existing labelled datasets, demonstrates improved results over the state of the art, especially for the cerebro-spinal fluid class which is the most difficult to label accurately.
authors.affiliation: University of California, Los Angeles, USA. zyi@ucla.edu
authors.forename: Zhao
authors.initials: Z
authors.lastname: Yi
authors.forename: Antonio
authors.initials: A
authors.lastname: Criminisi
authors.forename: Jamie
authors.initials: J
authors.lastname: Shotton
authors.forename: Andrew
authors.initials: A
authors.lastname: Blake
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / 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