Fast Multiple Organ Detection and Localization in Whole-Body MR Dixon Sequences

  • Olivier Pauly
  • Ben Glocker
  • Antonio Criminisi
  • Diana Mateus
  • Axel Martinez Möller
  • Stephan Nekolla
  • Nassir Navab
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

Automatic localization of multiple anatomical structures in medical images provides important semantic information with potential benefits to diverse clinical applications. Aiming at organ-specific attenuation correction in PET/MR imaging, we propose an efficient approach for estimating location and size of multiple anatomical structures in MR scans. Our contribution is three-fold: (1) we apply supervised regression techniques to the problem of anatomy detection and localization in whole-body MR, (2) we adapt random ferns to produce multi-dimensional regression output and compare them with random regression forests, and (3) introduce the use of 3D LBP descriptors in multi-channel MR Dixon sequences. The localization accuracy achieved with both fern- and forest-based approaches is evaluated by direct comparison with state of the art atlas-based registration, on ground-truth data from 33 patients. Our results demonstrate improved anatomy localization accuracy with higher efficiency and robustness.

Keywords

Random Forest Attenuation Correction Statistical Shape Model Regression Forest Voxel Location 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Olivier Pauly
    • 1
  • Ben Glocker
    • 2
  • Antonio Criminisi
    • 2
  • Diana Mateus
    • 1
  • Axel Martinez Möller
    • 3
  • Stephan Nekolla
    • 3
  • Nassir Navab
    • 1
  1. 1.Computer Aided Medical ProceduresTechnische Universität MünchenGermany
  2. 2.Microsoft Research Ltd.CambridgeUK
  3. 3.Nuklearmedizin, Klinikum Rechts der IsarTechnische Universität MünchenGermany

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