Automated Selection of Abdominal MRI Series Using a DICOM Metadata Classifier and Selective Use of a Pixel-Based Classifier

Chad M. Miller,Zhe Zhu,Maciej A. Mazurowski,Mustafa R. Bashir,Walter F. Wiggins

Abdominal Radiology（2024）

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Abstract

Accurate, automated MRI series identification is important for many applications, including display ("hanging") protocols, machine learning, and radiomics. The use of the series description or a pixel-based classifier each has limitations. We demonstrate a combined approach utilizing a DICOM metadata-based classifier and selective use of a pixel-based classifier to identify abdominal MRI series. The metadata classifier was assessed alone as Group metadata and combined with selective use of the pixel-based classifier for predictions with less than 70% certainty (Group combined). The overall accuracy (mean and 95% confidence intervals) for Groups metadata and combined on the test dataset were 0.870 CI (0.824,0.912) and 0.930 CI (0.893,0.963), respectively. With this combined metadata and pixel-based approach, we demonstrate accurate classification of 95% or greater for all pre-contrast MRI series and improved performance for some post-contrast series.

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Key words

Machine learning,Abdominal magnetic resonance imaging,DICOM metadata,Series classifier,Deep learning

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