title: Multimodal MRI segmentation of ischemic stroke lesions.
abstract: The problem addressed in this paper is the automatic segmentation of stroke lesions on MR multi-sequences. Lesions enhance differently depending on the MR modality and there is an obvious gain in trying to account for various sources of information in a single procedure. To this aim, we propose a multimodal Markov random field model which includes all MR modalities simultaneously. The results of the multimodal method proposed are compared with those obtained with a mono-dimensional segmentation applied on each MRI sequence separately. We constructed an Atlas of blood supply territories to help clinicians in the determination of stroke subtypes and potential functional deficit.
authors.affiliation: INSERM U836-UJF-CEA-CHU, Grenoble Institute of Neuroscience. yacine.kabir@imag.fr
authors.forename: Y
authors.initials: Y
authors.lastname: Kabir
authors.forename: M
authors.initials: M
authors.lastname: Dojat
authors.forename: B
authors.initials: B
authors.lastname: Scherrer
authors.forename: F
authors.initials: F
authors.lastname: Forbes
authors.forename: C
authors.initials: C
authors.lastname: Garbay
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D002545:Brain Ischemia / Q000209:etiology / 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; D020521:Stroke / Q000150:complications / Q000473:pathology*; D013382:Subtraction Technique