title: 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.
authors: Computer Aided Medical Procedures, Technische Universität München, Germany. pauly@cs.tum.edu
authors: Olivier
authors: O
authors: Pauly
authors: Ben
authors: B
authors: Glocker
authors: Antonio
authors: A
authors: Criminisi
authors: Diana
authors: D
authors: Mateus
authors: Axel Martinez
authors: AM
authors: Möller
authors: Stephan
authors: S
authors: Nekolla
authors: Nassir
authors: N
authors: Navab
mesh_terms: D000273:Adipose Tissue / Q000473:pathology; D000465:Algorithms; D001834:Body Water; D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D012044:Regression Analysis; D015203:Reproducibility of Results; D051598:Whole Body Imaging / Q000379:methods*