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

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Bazin,  Pierre-Louis
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Weiss,  Marcel
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Dinse,  Juliane
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Schäfer,  Andreas
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Trampel,  Robert
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Turner,  Robert
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

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.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-A7FB-C
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.