Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 34, Issue 10, Pages 2079-2102Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2015.2419072
Keywords
Bayesian inference criterion (BIC); brain segmentation; Gaussian mixture model (GMM); magnetic resonance imaging (MRI); split-and-merge (SM) strategy; white matter lesion (WML)
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Funding
- Wolfson Foundation
- Faculty of Engineering Science UCL
- EPSRC [EP/H046410/1, EP/J020990/1, EP/K005278]
- MRC [MR/J01107X/1]
- EU-FP7 project VPH-DARE IT [FP7-ICT-2011-9-601055]
- NIHR Queen Square Dementia Biomedical Research Unit
- NIHR UCLH/UCL Biomedical Research Centre (High Impact Initiative)
- Alzheimer's Research UK (ARUK)
- ARUK, Brain Research Trust
- EPSRC [EP/J020990/1, EP/H046410/1] Funding Source: UKRI
- MRC [MR/J01107X/1] Funding Source: UKRI
- Alzheimers Research UK [ARUK-SRF2013-5] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [EP/H046410/1, EP/J020990/1] Funding Source: researchfish
- Medical Research Council [MR/J01107X/1] Funding Source: researchfish
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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 freely-available automated methods.
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