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

  • O. Pauly ,
  • B. Glocker ,
  • A. Criminisi ,
  • N. Navab ,
  • D. Mateus ,
  • A. Martinez Moller ,
  • S. Nekolla ,
  • Antonio Criminisi ,
  • Ben Glocker

MICCAI 2011 - 14th International Conference on Medical Image Computing and Computer Assisted Intervention |

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 multidimensional 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