Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling.
Quantitative analysis of magnetic resonance imaging (MRI) scans of the brain requires accurate automated segmentation of anatomical structures. A desirable feature for such segmentation methods is to be robust against changes in acquisition platform and imaging protocol. In this paper we validate the performance of a segmentation algorithm designed to meet these requirements, building upon generative parametric models previously used in tissue classification. The method is tested on four different datasets acquired with different scanners, field strengths and pulse sequences, demonstrating comparable accuracy to state-of-the-art methods on T1-weighted scans while being one to two orders of magnitude faster. The proposed algorithm is also shown to be robust against small training datasets, and readily handles images with different MRI contrast as well as multi-contrast data.
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby, Denmark. Electronic address: oupu@dtu.dk.
Oula
O
Puonti
Basque Center on Cognition, Brain and Language (BCBL), Paseo Mikeletegi, 20009 San Sebastian - Donostia, Gipuzkoa, Spain; Department of Medical Physics and Biomedical Engineering, University College London, Gower St, London WC1E 6BT, United Kingdom.
Juan Eugenio
JE
Iglesias
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby, Denmark; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129, USA.
Koen
K
Van Leemput
D000328:Adult; D001499:Bayes Theorem; D001921:Brain / Q000000981:diagnostic imaging*; D066264:Datasets as Topic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical
Atlases; Bayesian modeling; MRI; Parametric models; Segmentation