期刊
FRONTIERS IN NEUROINFORMATICS
卷 10, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fninf.2016.00052
关键词
structural magnetic resonance imaging; database management; automated quality assessment; machine learning; support vector machine; artifact detection; region of interest
资金
- Intramural Research Program of the National Institute of Mental Health, NUL Bethesda, MD, USA
- Office of Science Management and Operations (OSMO) of the NIAID, Bethesda, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- National Institute of General Medical Sciences [R25GM083252]
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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