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Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm.
Pizarro, Ricardo A; Cheng, Xi; Barnett, Alan; Lemaitre, Herve; Verchinski, Beth A; Goldman, Aaron L; Xiao, Ena; Luo, Qian; Berman, Karen F; Callicott, Joseph H; Weinberger, Daniel R; Mattay, Venkata S.
Afiliación
  • Pizarro RA; Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA; Department of Biomedical Engineering, UW-MadisonMadison, WI, USA.
  • Cheng X; Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA; The Lieber Institute for Brain DevelopmentBaltimore, MD, USA; Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology (O
  • Barnett A; Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of Health Bethesda, MD, USA.
  • Lemaitre H; Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA; NeuroImaging and Psychiatry, UMR 1000, Faculté de Médecine, Institut National de la Santé Et de la Recherche Médicale, Service Hospitalier Frédéric Joliot, Université Paris-S
  • Verchinski BA; Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA; The Lieber Institute for Brain DevelopmentBaltimore, MD, USA.
  • Goldman AL; Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA; The Lieber Institute for Brain DevelopmentBaltimore, MD, USA.
  • Xiao E; Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA; The Lieber Institute for Brain DevelopmentBaltimore, MD, USA.
  • Luo Q; Behavioral Biology Branch, Walter Reed Army Research Institute Silver Spring, MD, USA.
  • Berman KF; Clinical and Translational Neuroscience Branch, National Institute of Mental Health, National Institutes of Health Bethesda, MD, USA.
  • Callicott JH; Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA; Clinical and Translational Neuroscience Branch, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA.
  • Weinberger DR; Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA; The Lieber Institute for Brain DevelopmentBaltimore, MD, USA; Departments of Psychiatry, Neurology and Neuroscience, Johns Hopkins University School of MedicineBaltimore, MD,
  • Mattay VS; Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA; The Lieber Institute for Brain DevelopmentBaltimore, MD, USA; Departments of Neurology and Radiology, Johns Hopkins University School of MedicineBaltimore, MD, USA.
Front Neuroinform ; 10: 52, 2016.
Article en En | MEDLINE | ID: mdl-28066227
ABSTRACT
High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Neuroinform Año: 2016 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
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PubMed Links
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Neuroinform Año: 2016 Tipo del documento: Article País de afiliación: Estados Unidos