Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks

Journal of Magnetic Resonance Imaging : JMRI
Sheeba J SujitRefaat E Gabr

Abstract

Deep learning (DL) is a promising methodology for automatic detection of abnormalities in brain MRI. To automatically evaluate the quality of multicenter structural brain MRI images using an ensemble DL model based on deep convolutional neural networks (DCNNs). Retrospective. 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. T1 -weighted MR brain images acquired at 3T. 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. Receiver operating characteristic (ROC) curve, area under ROC curve (AUC), sensitiv...Continue Reading

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Citations

Feb 15, 2021·Artificial Intelligence in Medicine·Alberto NogalesCristina Antón
Apr 24, 2021·Journal of Magnetic Resonance Imaging : JMRI·Axel LargentCatherine Limperopoulos
May 15, 2021·La Presse médicale·Jean-Christophe BrissetUNKNOWN Imaging Group of the 'Observatoire francais de la sclérose en plaques'
Sep 2, 2021·Computer Methods and Programs in Biomedicine·Sheeba J SujitLuca Giancardo
Oct 12, 2021·Physical and Engineering Sciences in Medicine·Yujiro DoiHiroshi Fujita

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