title: Example Based Lesion Segmentation.
abstract: Automatic and accurate detection of white matter lesions is a significant step toward understanding the progression of many diseases, like Alzheimer's disease or multiple sclerosis. Multi-modal MR images are often used to segmentT2white matter lesions that can represent regions of demyelination or ischemia. Some automated lesion segmentation methods describe the lesion intensities using generative models, and then classify the lesions with some combination of heuristics and cost minimization. In contrast, we propose a patch-based method, in which lesions are found using examples from an atlas containing multi-modal MR images and corresponding manual delineations of lesions. Patches from subject MR images are matched to patches from the atlas and lesion memberships are found based on patch similarity weights. We experiment on 43 subjects with MS, whose scans show various levels of lesion-load. We demonstrate significant improvement in Dice coefficient and total lesion volume compared to a state of the art model-based lesion segmentation method, indicating more accurate delineation of lesions.
authors: Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation for the Advancement of Military Medicine, USA.
authors: Snehashis
authors: S
authors: Roy
authors: Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation for the Advancement of Military Medicine, USA.
authors: Qing
authors: Q
authors: He
authors: Department of Electrical and Computer Engineering, The Johns Hopkins University, USA.
authors: Aaron
authors: A
authors: Carass
authors: Department of Electrical and Computer Engineering, The Johns Hopkins University, USA.
authors: Amod
authors: A
authors: Jog
authors: Department of Radiology, The Johns Hopkins School of Medicine, USA.
authors: Jennifer L
authors: JL
authors: Cuzzocreo
authors: Translational Neuroradiology Unit, Neuroimmunology Branch, National Institute of Neurological Disorders and Stroke, USA.
authors: Daniel S
authors: DS
authors: Reich
authors: Department of Electrical and Computer Engineering, The Johns Hopkins University, USA.
authors: Jerry
authors: J
authors: Prince
authors: Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation for the Advancement of Military Medicine, USA.
authors: Dzung
authors: D
authors: Pham
keywords: MRI; MS; lesion segmentation; magnetic resonance imaging; patches