Abstract
This paper presents a computational framework for whole brain segmentation of 7. Tesla 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.
Original language | English (US) |
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Pages (from-to) | 201-209 |
Number of pages | 9 |
Journal | NeuroImage |
Volume | 93 |
DOIs | |
State | Published - Jun 1 2014 |
Externally published | Yes |
Keywords
- 7 Tesla MRI
- Ultra-high resolution
- Whole brain segmentation
ASJC Scopus subject areas
- Neurology
- Cognitive Neuroscience