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Publication Detail
Bayesian model selection for pathological neuroimaging data applied to white matter lesion segmentation
  • Publication Type:
    Journal article
  • Authors:
    Sudre CH, Cardoso MJ, Bouvy WH, Biessels GJ, Barnes J, Ourselin S
  • Addresses:
    Translational Imaging Group, Centre for Medical Image Computing, University College London

    Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London

    Brain Center Rudolf Magnus, Department of Neurology, University Medical Center Utrecht,
    Utrecht, the Netherlands
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.
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Neurodegenerative Diseases
Dept of Med Phys & Biomedical Eng
Dept of Med Phys & Biomedical Eng
Dept of Med Phys & Biomedical Eng
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