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

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Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Chad M MillerDuke University School of Medicine, Durham, NC, 27710, USA. chad.miller@duke.edu.ORCID 0000-0002-9896-470X

Zhe ZhuDuke University School of Medicine, Durham, NC, 27710, USA.

Maciej A MazurowskiDuke University School of Medicine, Durham, NC, 27710, USA.

Mustafa R BashirDuke University School of Medicine, Durham, NC, 27710, USA.

Walter F WigginsDuke University School of Medicine, Durham, NC, 27710, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AbdomenMagnetic Resonance ImagingMetadataRadiology Information SystemsHumansImage Interpretation, Computer-AssistedAbdominal magnetic resonance imagingDeep learningDICOM metadataMachine learningSeries classifier

Identifiers

PMID38860997

DOI10.1109/TMI.2010.2063711

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.