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  A computational framework for ultra-high resolution cortical segmentation at 7 Tesla

Bazin, P.-L., Weiss, M., Dinse, J., Schäfer, A., Trampel, R., & Turner, R. (2014). A computational framework for ultra-high resolution cortical segmentation at 7 Tesla. NeuroImage, 93(2), 201-209. doi:10.1016/j.neuroimage.2013.03.077.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-A7FB-C Version Permalink: http://hdl.handle.net/21.11116/0000-0003-7EFD-4
Genre: Journal Article

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 Creators:
Bazin, Pierre-Louis1, 2, Author              
Weiss, Marcel1, Author              
Dinse, Juliane1, Author              
Schäfer, Andreas1, Author              
Trampel, Robert1, Author              
Turner, Robert1, Author              
Affiliations:
1Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634550              
2Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_634549              

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Free keywords: Whole brain segmentation; Ultra-high resolution; 7 Tesla MRI
 Abstract: This paper presents a computational framework for whole brain segmentation of 7Tesla magnetic resonance images able to handle ultra-high resolution data. The approach combines multi-object topology-preserving deformable models with shape and intensity atlases to encode prior anatomical knowledge in a computationally efficient algorithm. Experimental validation on simulated and real brain images shows accuracy and robustness of the method and demonstrates the benefits of an increased processing resolution.

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Language(s): eng - English
 Dates: 2013-03-292013-04-252014-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2013.03.077
PMID: 23623972
Other: Epub 2013
 Degree: -

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Title: NeuroImage
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 93 (2) Sequence Number: - Start / End Page: 201 - 209 Identifier: ISSN: 1053-8119
CoNE: /journals/resource/954922650166