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Bayesian model selection for pathological neuroimaging data applied to white matter lesion segmentation.

Sudre, C; Cardoso, MJ; Bouvy, W; Biessels, G; Barnes, J; Ourselin, S; (2015) Bayesian model selection for pathological neuroimaging data applied to white matter lesion segmentation. IEEE Transactions on Medical Imaging , 34 (10) pp. 2079-2102. 10.1109/TMI.2015.2419072. Green open access

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Abstract

In neuroimaging studies, pathologies can present themselves as abnormal intensity patterns. Thus, solutions for detecting abnormal intensities are currently under investigation. As each patient is unique, an unbiased and biologically plausible model of pathological data would have to be able to adapt to the subject's individual presentation. Such a model would provide the means for a better understanding of the underlying biological processes and improve one's ability to define pathologically meaningful imaging biomarkers. With this aim in mind, this work proposes a hierarchical fully unsupervised model selection framework for neuroimaging data which enables the distinction between different types of abnormal image patterns without pathological a priori knowledge. Its application on simulated and clinical data demonstrated the ability to detect abnormal intensity clusters, resulting in a competitive to improved behavior in white matter lesion segmentation when compared to three other freelyavailable automated methods.

Type: Article
Title: Bayesian model selection for pathological neuroimaging data applied to white matter lesion segmentation.
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TMI.2015.2419072
Publisher version: http://dx.doi.org/10.1109/TMI.2015.2419072
Language: English
Additional information: Copyright © 2015 IEEE. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
Keywords: Bayesian inference criterion (BIC), brain segmentation, Gaussian mixture model (GMM), magnetic resonance imaging (MRI), split-and-merge (SM) strategy, white matter lesion (WML).
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine > MRC Unit for Lifelong Hlth and Ageing
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1466255
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