Automated selection of abdominal MRI series using a DICOM metadata classifier and selective use of a pixel-based classifier.
Abdom Radiol (NY)
; 49(10): 3735-3746, 2024 Oct.
Article
em En
| MEDLINE
| ID: mdl-38860997
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Imageamento por Ressonância Magnética
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Sistemas de Informação em Radiologia
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Abdome
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Metadados
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article