Scholars@Duke publication: Automated selection of abdominal MRI series using a DICOM metadata classifier and selective use of a pixel-based classifier.

Automated selection of abdominal MRI series using a DICOM metadata classifier and selective use of a pixel-based classifier.

Publication , Journal Article

Miller, CM; Zhu, Z; Mazurowski, MA; Bashir, MR; Wiggins, WF

Published in: Abdom Radiol (NY)

June 11, 2024

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.