A Multi-Atlas and Label Fusion Approach for Patient-Specific MRI Based Skull Segmentation

Angel Torrado-Carvajal (1, 2) , Joaquin Lopez Herraiz (3, 2) , Juan Antonio Hernández-Tamames (1, 2) , Raul San Jose-Estepar (4, 2) , Yigitcan Eryaman (3, 5, 2) , Yves Rozenholc (6, 7) , Elfar Adalsteinsson (2, 8, 9, 10) , Lawrence L. Wald (5, 9) , Norberto Malpica (1, 2)

1 URJC - Universidad Rey Juan Carlos [Madrid]
2 Madrid-MIT m+Vision Consortium
3 RLE - Research Laboratory of Electronics [Cambridge]
4 Department of Radiology [Boston]
5 Athinoula A. Martinos Center for Biomedical Imaging
6 MAP5 - UMR 8145 - Mathématiques Appliquées Paris 5
7 SELECT - Model selection in statistical learning
8 IMES - Institute of Medical Engineering and Science [Cambridge]
9 Harvard-MIT Division of Health Sciences and Technology [Cambridge]
10 EECS - Department of Electrical Engineering and Computer Science

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, 2015. © 2015 Wiley Periodicals, Inc.

Domaines: Imagerie; Traitement du signal et de l'image [eess.SP]

Angel Torrado-Carvajal, Joaquin Lopez Herraiz, Juan Antonio Hernández-Tamames, Raul San Jose-Estepar, Yigitcan Eryaman, et al.. A Multi-Atlas and Label Fusion Approach for Patient-Specific MRI Based Skull Segmentation. International Society for Magnetic Resonance in Medicine (ISMRM), 2014, May 2014, Milan, Italy. ⟨10.1002/mrm.25737⟩. ⟨hal-01108453⟩