Angel Torrado-Carvajal1,2, Joaquin L Herraiz2,3, Juan A Hernandez-Tamames1,2, Raul San Jose-Estepar2,4, Yigitcan Eryaman2,3,5, Yves Rozenholc6,7, Elfar Adalsteinsson2,8,9,10, Lawrence L Wald5,9, Norberto Malpica1,2. 1. Medical Image Analysis and Biometry Lab, Universidad Rey Juan Carlos, Mostoles, Madrid, Spain. 2. Madrid-MIT M+Vision Consortium, Madrid, Spain. 3. Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. 4. Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA. 5. A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA. 6. MAP5, CNRS UMR 8145, University Paris Descartes, Paris, France. 7. INRIA Saclay - Ile de France - SELECT, Paris, France. 8. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. 9. Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. 10. Institute of Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
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.
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.
Authors: Bastien Guérin; Jason P Stockmann; Mehran Baboli; Angel Torrado-Carvajal; Andrew V Stenger; Lawrence L Wald Journal: Magn Reson Med Date: 2015-10-07 Impact factor: 4.668