title: Multiple sclerosis lesion segmentation using an automatic multimodal graph cuts.
abstract: Graph Cuts have been shown as a powerful interactive segmentation technique in several medical domains. We propose to automate the Graph Cuts in order to automatically segment Multiple Sclerosis (MS) lesions in MRI. We replace the manual interaction with a robust EM-based approach in order to discriminate between MS lesions and the Normal Appearing Brain Tissues (NABT). Evaluation is performed in synthetic and real images showing good agreement between the automatic segmentation and the target segmentation. We compare our algorithm with the state of the art techniques and with several manual segmentations. An advantage of our algorithm over previously published ones is the possibility to semi-automatically improve the segmentation due to the Graph Cuts interactive feature.
authors.affiliation: INRIA, VisAGeS Unit/Project, IRISA, Rennes, France.
authors.forename: Daniel
authors.initials: D
authors.lastname: García-Lorenzo
authors.forename: Jeremy
authors.initials: J
authors.lastname: Lecoeur
authors.forename: Douglas L
authors.initials: DL
authors.lastname: Arnold
authors.forename: D Louis
authors.initials: DL
authors.lastname: Collins
authors.forename: Christian
authors.initials: C
authors.lastname: Barillot
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / 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