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Multi‐atlas and label fusion approach for patient‐specific MRI based skull estimation
Author(s) -
TorradoCarvajal Angel,
Herraiz Joaquin L.,
HernandezTamames Juan A.,
San JoseEstepar Raul,
Eryaman Yigitcan,
Rozenholc Yves,
Adalsteinsson Elfar,
Wald Lawrence L.,
Malpica Norberto
Publication year - 2016
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.25737
Subject(s) - computer science , segmentation , atlas (anatomy) , artificial intelligence , skull , image fusion , magnetic resonance imaging , computer vision , image segmentation , real time mri , correction for attenuation , ground truth , image registration , nuclear medicine , pattern recognition (psychology) , radiology , positron emission tomography , medicine , image (mathematics) , anatomy
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 ± 6.99%), a clinical CT‐MR dataset (maximum overlap of 78.31 ± 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 75:1797–1807, 2016. © 2015 Wiley Periodicals, Inc.