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Proceedings Paper

Example based lesion segmentation
Author(s): Snehashis Roy; Qing He; Aaron Carass; Amod Jog; Jennifer L. Cuzzocreo; Daniel S. Reich; Jerry Prince; Dzung Pham
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Paper 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 segment T2 white 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.

Paper Details

Date Published: 21 March 2014
PDF: 8 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90341Y (21 March 2014); doi: 10.1117/12.2043917
Show Author Affiliations
Snehashis Roy, Henry M. Jackson Foundation (United States)
Qing He, Henry M. Jackson Foundation (United States)
Aaron Carass, Johns Hopkins Univ. (United States)
Amod Jog, Johns Hopkins Univ. (United States)
Jennifer L. Cuzzocreo, Johns Hopkins Univ. School of Medicine (United States)
Daniel S. Reich, National Institute of Neurological Disorders and Stroke (United States)
Jerry Prince, Johns Hopkins Univ. (United States)
Dzung Pham, Henry M. Jackson Foundation (United States)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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