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Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks
Author(s) -
Sujit Sheeba J.,
Coronado Ivan,
Kamali Arash,
Narayana Ponnada A.,
Gabr Refaat E.
Publication year - 2019
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.26693
Subject(s) - artificial intelligence , convolutional neural network , receiver operating characteristic , image quality , computer science , pattern recognition (psychology) , artificial neural network , ensemble learning , deep learning , sagittal plane , data set , magnetic resonance imaging , population , test set , coronal plane , set (abstract data type) , machine learning , image (mathematics) , medicine , radiology , environmental health , programming language
Background Deep learning (DL) is a promising methodology for automatic detection of abnormalities in brain MRI. Purpose To automatically evaluate the quality of multicenter structural brain MRI images using an ensemble DL model based on deep convolutional neural networks (DCNNs). Study Type Retrospective. Population The study included 1064 brain images of autism patients and healthy controls from the Autism Brain Imaging Data Exchange (ABIDE) database. MRI data from 110 multiple sclerosis patients from the CombiRx study were included for independent testing. Sequence T 1 ‐weighted MR brain images acquired at 3T. Assessment The ABIDE data were separated into training (60%), validation (20%), and testing (20%) sets. The ensemble DL model combined the results from three cascaded networks trained separately on the three MRI image planes (axial, coronal, and sagittal). Each cascaded network consists of a DCNN followed by a fully connected network. The quality of image slices from each plane was evaluated by the DCNN and the resultant image scores were combined into a volumewise quality rating using the fully connected network. The DL predicted ratings were compared with manual quality evaluation by two experts. Statistical Tests Receiver operating characteristic (ROC) curve, area under ROC curve (AUC), sensitivity, specificity, accuracy, and positive (PPV) and negative (NPV) predictive values. Results The AUC, sensitivity, specificity, accuracy, PPV, and NPV for image quality evaluation of the ABIDE test set using the ensemble model were 0.90, 0.77, 0.85, 0.84, 0.42, and 0.96, respectively. On the CombiRx set the same model achieved performance of 0.71, 0.41, 0.84, 0.73, 0.48, and 0.80. Data Conclusion This study demonstrated the high accuracy of DL in evaluating image quality of structural brain MRI in multicenter studies. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1260–1267.