@article {472951,
	title = {Multi-atlas and label fusion approach for patient-specific MRI based skull estimation},
	journal = {Magnetic Resonance in MedicineMagnetic Resonance in Medicine},
	year = {2015},
	month = {Jun 18},
	pages = {n/a-n/a},
	abstract = {PURPOSE:MRI-based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1-weighted volume.METHODS:The skull is estimated using a multi-atlas segmentation approach. Using a whole head computed tomography (CT) scan database, the skull in a new MRI volume is detected by nonrigid image registration of the volume to every CT, and combination of the individual segmentations by label-fusion. We have compared Majority Voting, Simultaneous Truth and Performance Level Estimation (STAPLE), Shape Based Averaging (SBA), and the Selective and Iterative Method for Performance Level Estimation (SIMPLE) algorithms.RESULTS:The pipeline has been evaluated quantitatively using images from the Retrospective Image Registration Evaluation database (reaching an overlap of 72.46 {\textpm} 6.99\%), a clinical CT-MR dataset (maximum overlap of 78.31 {\textpm} 6.97\%), and a whole head CT-MRI pair (maximum overlap 78.68\%). A qualitative evaluation has also been performed on MRI acquisition of volunteers.CONCLUSION:It is possible to automatically segment the complete skull from MRI data using a multi-atlas and label fusion approach. This will allow the creation of complete MRI-based tissue models that can be used in electromagnetic dosimetry applications and attenuation correction in PET/MR. Magn Reson Med, 2015. {\textcopyright} 2015 Wiley Periodicals, Inc.},
	author = {Torrado-Carvajal, Angel and Herraiz, Joaquin L and Hernandez-Tamames, Juan A and San Jose Est{\'e}par, Ra{\'u}l and Eryaman, Yigitcan and Rozenholc, Yves and Adalsteinsson, Elfar and Wald, Lawrence L and Malpica, Norberto}
}