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.
Bibliographical noteFunding Information:
Comunidad de Madrid; Grant sponsor: Madrid-MIT M1Vision Consortium. The authors thank Dr. Juan Alvarez-Linera from Ruber International Hospital in Madrid for kindly providing the clinical datasets, and the anonymous reviewers for their valuable comments and suggestions that helped to improve the quality of the study.
© 2015 Wiley Periodicals, Inc.
Copyright 2020 Elsevier B.V., All rights reserved.
- label fusion
- skull segmentation
- tissue models