Advertisement

Data-Parallel MRI Brain Segmentation in Clinical Use

Porting FSL-FASTv4 to GPGPUs
  • Joachim Weber
  • Christian Doenitz
  • Alexander Brawanski
  • Christoph Palm
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Abstract

Structural MRI brain analysis and segmentation is a crucial part in the daily routine in neurosurgery for intervention planning. Exemplarily, the free software FSL-FAST (FMRIB’s Segmentation Library – FMRIB’s Automated Segmentation Tool) in version 4 is used for segmentation of brain tissue types. To speed up the segmentation procedure by parallel execution, we transferred FSL-FAST to a General Purpose Graphics Processing Unit (GPGPU) using Open Computing Language (OpenCL) [1]. The necessary steps for parallelization resulted in substantially different and less useful results. Therefore, the underlying methods were revised and adapted yielding computational overhead. Nevertheless, we achieved a speed-up factor of 3.59 from CPU to GPGPU execution, as well providing similar useful or even better results.

Keywords

Segmentation Result Magnetic Resonance Image Brain Parallel Execution Voxel Spacing General Purpose Graphic Processing Unit 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literaturverzeichnis

  1. 1.
    Group KOW. The OpenCL Specification 1.1; 2008.Google Scholar
  2. 2.
    Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging. 2001;20(1):45–57.CrossRefGoogle Scholar
  3. 3.
    Committee MS. IEEE Standard for Floating-Point Arithmetic. IEEE Computer Society; 2008.Google Scholar
  4. 4.
    Higham NJ. The accuracy of floating point summation. SIAM J Sci Comput. 1993;14(4):783–99.CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Besag J. On the statistical analysis of dirty pictures. J R Stat Soc Ser B. 1986;48(3):259–302.zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Joachim Weber
    • 1
  • Christian Doenitz
    • 2
  • Alexander Brawanski
    • 2
  • Christoph Palm
    • 1
    • 3
  1. 1.Regensburg – Medical Image Computing (ReMIC) Ostbayerische Technische Hochschule Regensburg (OTH Regensburg)RegensburgDeutschland
  2. 2.Department of NeurosurgeryUniversity Medical Center RegensburgRegensburgDeutschland
  3. 3.Regensburg Center of Biomedical Engineering (RCBE)OTH Regensburg and Regensburg UniversityRegensburgDeutschland

Personalised recommendations