title: Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR.
abstract: We present a method for automatic segmentation of high-grade gliomas and their subregions from multi-channel MR images. Besides segmenting the gross tumor, we also differentiate between active cells, necrotic core, and edema. Our discriminative approach is based on decision forests using context-aware spatial features, and integrates a generative model of tissue appearance, by using the probabilities obtained by tissue-specific Gaussian mixture models as additional input for the forest. Our method classifies the individual tissue types simultaneously, which has the potential to simplify the classification task. The approach is computationally efficient and of low model complexity. The validation is performed on a labeled database of 40 multi-channel MR images, including DTI. We assess the effects of using DTI, and varying the amount of training data. Our segmentation results are highly accurate, and compare favorably to the state of the art.
authors.affiliation: Microsoft Research Cambridge, UK
authors.forename: Darko
authors.initials: D
authors.lastname: Zikic
authors.forename: Ben
authors.initials: B
authors.lastname: Glocker
authors.forename: Ender
authors.initials: E
authors.lastname: Konukoglu
authors.forename: Antonio
authors.initials: A
authors.lastname: Criminisi
authors.forename: C
authors.initials: C
authors.lastname: Demiralp
authors.forename: J
authors.initials: J
authors.lastname: Shotton
authors.forename: O M
authors.initials: OM
authors.lastname: Thomas
authors.forename: T
authors.initials: T
authors.lastname: Das
authors.forename: R
authors.initials: R
authors.lastname: Jena
authors.forename: S J
authors.initials: SJ
authors.lastname: Price
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001932:Brain Neoplasms / Q000473:pathology*; D020000:Decision Support Systems, Clinical; D005910:Glioma / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity