Multi-atlas and label fusion approach for patient-specific MRI based skull estimation

Angel Torrado-Carvajal, Joaquin L. Herraiz, Juan A. Hernandez-Tamames, Raul San Jose-Estepar, Yigitcan Eryaman, Yves Rozenholc, Elfar Adalsteinsson, Lawrence L. Wald, Norberto Malpica

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1797-1807
Number of pages11
JournalMagnetic resonance in medicine
Issue number4
StatePublished - Apr 1 2016

Bibliographical note

Funding 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.

Publisher Copyright:
© 2015 Wiley Periodicals, Inc.


  • MRI
  • atlas-based
  • label fusion
  • skull segmentation
  • tissue models


Dive into the research topics of 'Multi-atlas and label fusion approach for patient-specific MRI based skull estimation'. Together they form a unique fingerprint.

Cite this