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bioRxiv · 10.1101/2020.10.23.352781

Machine Learning Framework For Fully Automatic Quality Checking Of Rigid And Affine Registrations In Big Data Brain MRI

Tummala, S.
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Abstract

Rigid and affine registrations to a common template are the essential steps during pre-processing of brain structural magnetic resonance imaging (MRI) data. Manual quality check (QC) of these registrations is quite tedious if the data contains several thousands of images. Therefore, we propose a machine learning (ML) framework for fully automatic QC of these registrations via local computation of the similarity functions such as normalized cross-correlation, normalized mutual-information, and correlation ratio, and using these as features for training of different ML classifiers. To facilitate supervised learning, misaligned images are generated. A structural MRI dataset consisting of 215 subjects from autism brain imaging data exchange is used for 5-fold cross-validation and testing. Few classifiers such as kNN, AdaBoost, and random forest reached testing F1-scores of 0.98 for QC of both rigid and affine registrations. These tested ML models could be deployed for practical use.

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Tummala, S.. 2020-10-25. Machine Learning Framework For Fully Automatic Quality Checking Of Rigid And Affine Registrations In Big Data Brain MRI. https://doi.org/10.1101/2020.10.23.352781

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