@inproceedings{0c3d73ae693a451b8ead033b368239af,
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 Alzheimers 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.",
keywords = "Lesion segmentation, MRI, MS, Magnetic resonance imaging, Patches",
author = "Snehashis Roy and Qing He and Aaron Carass and Amod Jog and Cuzzocreo, {Jennifer L.} and Reich, {Daniel S.} and Jerry Prince and Dzung Pham",
year = "2014",
doi = "10.1117/12.2043917",
language = "English (US)",
isbn = "9780819498274",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
booktitle = "Medical Imaging 2014",
note = "Medical Imaging 2014: Image Processing ; Conference date: 16-02-2014 Through 18-02-2014",
}