Supervised machine learning quality control for magnetic resonance artifacts in neonatal data sets

AbstractQuality control (QC) of brain magnetic resonance images (MRI) is an important process requiring a significant amount of manual inspection. Major artifacts, such as severe subject motion, are easy to identify to na ïve observers but lack automated identification tools. Clinical trials involving motion‐prone neonates typically pool data to obtain sufficient power, and automated quality control protocols are especially important to safeguard data quality. Current study tested an open source method to detect m ajor artifacts among 2D neonatal MRI via supervised machine learning. A total of 1,020 two‐dimensional transverse T2‐weighted MRI images of preterm newborns were examined and classified as either QC Pass or QC Fail. Then 70 features across focus, texture, noise, and natural scene statistics cate gories were extracted from each image. Several different classifiers were trained and their performance was compared with subjective rating as the gold standard. We repeated the rating process again to examine the stability of the rating and classification. When tested via 10‐fold cross validation , the random undersampling and adaboost ensemble (RUSBoost) method achieved the best overall performance for QC Fail images with 85% positive predictive value along with 75% sensitivity. Similar classification performance was observed in the analyses of the repeated subjective rating. Current result s served as a proof of concept for predicting images that fail quality con...
Source: Human Brain Mapping - Category: Neurology Authors: Tags: RESEARCH ARTICLE Source Type: research