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|a Dalca, Adrian Vasile
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Dalca, Adrian Vasile
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|a Golland, Polina
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|a Danagoulian, Giovanna
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|a Kikinis, Ron
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|a Schmidt, Ehud
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|a Golland, Polina
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|a Segmentation of nerve bundles and ganglia in spine MRI using particle filters
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|b Springer Berlin/Heidelberg,
|c 2012-09-27T21:42:46Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/73456
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|a 14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part III
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|a Automatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation.
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|a National Institutes of Health (U.S.) (NAMIC award U54-EB005149)
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|a National Science Foundation (U.S.) (CAREER grant 0642971)
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|a Article
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|t Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011
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