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

Magn Reson Med. 2016 Apr;75(4):1797-807. doi: 10.1002/mrm.25737. Epub 2015 May 18.


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.

Keywords: MRI; atlas-based; label fusion; skull segmentation; tissue models.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms
  • Brain / anatomy & histology
  • Brain / diagnostic imaging
  • Brain / pathology
  • Brain Diseases / diagnostic imaging
  • Brain Diseases / pathology
  • Brain Mapping / methods*
  • Female
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Magnetic Resonance Imaging / methods*
  • Male
  • Middle Aged
  • Retrospective Studies
  • Skull / anatomy & histology*
  • Skull / diagnostic imaging*
  • User-Computer Interface
  • Young Adult