Multi-atlas and label fusion approach for patient-specific MRI based skull estimation
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Multi-atlas and label.pdf
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4.6 MB
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Checksum (MD5)
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Author(s)
Torrado-Carvajal, Angel
Herraiz, Joaquin L.
Hernandez-Tamames, Juan A.
San Jose-Estepar, Raul
Eryaman, Yigitcan
Rozenholc, Yves
Adalsteinsson, Elfar
Wald, Lawrence L.
Malpica, Norberto
Date Issued
March 2016
Journal
Magnetic Resonance in Medicine
Publisher
Wiley Blackwell
Citation
Torrado-Carvajal, Angel; Herraiz, Joaquin L.; Hernandez-Tamames, Juan A. et al. “Multi-Atlas and Label Fusion Approach for Patient-Specific MRI Based Skull Estimation.” Magnetic Resonance in Medicine 75, 4 (May 2015): 1797–1807 © 2015 Wiley Periodicals, Inc
Version
Author's final manuscript
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 ± 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.
MIT Department
Institute for Medical Engineering and Science
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1002/mrm.25737
