Learning Global and Cluster-Specific Classifiers for Robust Brain Extraction in MR Data

  • Yuan Liu
  • Hasan E. ÇetingülEmail author
  • Benjamin L. Odry
  • Mariappan S. Nadar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)


We present a learning-based framework for automatic brain extraction in MR images. It accepts single or multi-contrast brain MR data, builds global binary random forests classifiers at multiple resolution levels, hierarchically performs voxelwise classifications for a test subject, and refines the brain surface using a narrow-band level set technique on the classification map. We further develop a data-driven schema to improve the model performance, which clusters patches of co-registered training images and learns cluster-specific classifiers. We validate our framework via experiments on single and multi-contrast datasets acquired using scanners with different magnetic field strengths. Compared to the state-of-the-art methods, it yields the best performance with statistically significant improvement of the cluster-specific method (with a Dice coefficient of 97.6 ± 0.4 % and an average surface distance of 0.8 ± 0.1 mm) over the global method.


Training Sample Classification Score Affinity Propagation Dice Coefficient Random Forest Classifier 
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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Yuan Liu
    • 1
    • 2
  • Hasan E. Çetingül
    • 2
    Email author
  • Benjamin L. Odry
    • 2
  • Mariappan S. Nadar
    • 2
  1. 1.Vanderbilt Institute in Surgery and EngineeringVanderbilt UniversityNashvilleUSA
  2. 2.Medical Imaging TechnologiesSiemens HealthineersPrincetonUSA

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