Automated selection of abdominal MRI series using a DICOM metadata classifier and selective use of a pixel-based classifier.
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 metadat...
| Publicado en: | Abdominal Radiology Vol. 49; no. 10; pp. 3735 - 3747 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179574376&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179574376 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Oct2024 vid: 49 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179574376 177793200 10.1007/s00261-024-04379-5 179574376 ppf: 3735 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated selection of abdominal MRI series using a DICOM metadata classifier and selective use of a pixel-based classifier. aug: au: Miller, Chad M. Zhu, Zhe Mazurowski, Maciej A. Bashir, Mustafa R. Wiggins, Walter F. affil: Duke University School of Medicine, 27710, Durham, NC, USA sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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