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Fast multiple organ detection and localization in whole-body MR dixon sequences.

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 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.
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https://hal.inria.fr/hal-01690326
Contributor : Diana Mateus <>
Submitted on : Monday, January 22, 2018 - 10:02:49 PM
Last modification on : Sunday, April 22, 2018 - 4:06:02 PM

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  • HAL Id : hal-01690326, version 1
  • PUBMED : 22003705

Citation

Olivier Pauly, Ben Glocker, Antonio Criminisi, Diana Mateus, Axel Martinez Möller, et al.. Fast multiple organ detection and localization in whole-body MR dixon sequences.. International Conference on Medical Image Computing and Computer Aided Interventions (MICCAI), Sep 2011, Toronto, Canada. pp.239-47. ⟨hal-01690326⟩

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