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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)

Abstract

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.

Keywords

Training Sample Classification Score Affinity Propagation Dice Coefficient Random Forest Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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