We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Automated selection of abdominal MRI series using a DICOM metadata classifier and selective use of a pixel-based classifier.
- Authors
Miller, Chad M.; Zhu, Zhe; Mazurowski, Maciej A.; Bashir, Mustafa R.; Wiggins, Walter F.
- 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.
- Subjects
MAGNETIC resonance imaging; MACHINE learning; DEEP learning; RADIOMICS; CONFIDENCE intervals; METADATA
- Publication
Abdominal Radiology, 2024, Vol 49, Issue 10, p3735
- ISSN
2366-004X
- Publication type
Article
- DOI
10.1007/s00261-024-04379-5