Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.23.352781v1?rss=1 Authors: Tummala, S. Abstract: Rigid and affine registration to a common template is one of the vital steps during pre-processing of brain structural magnetic resonance imaging (MRI) data to make them suitable for further processing. Manual quality check (QC) of these registrations is tedious if the data contains several thousands of images. Therefore, I propose a machine learning (ML) framework for fully automatic QC of registrations by local computation of the similarity cost functions such as normalized cross-correlation, normalized mutual-information and correlation ratio, making them as features in training of ML classifiers. A MRI dataset consisting of 220 subjects from autism brain imaging data exchange is used for 5-fold cross-validation and testing. To facilitate supervised learning, the misaligned images were generated. Most of the classifiers reached testing F1-scores of 0.98 for checking rigid and affine registrations. Therefore, these ML models could be deployed for practical use. Copy rights belong to original authors. Visit the link for more info