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Automated liver segmental volume ratio quantification on non-contrast T1-Vibe Dixon liver MRI using deep learning.
Zbinden, Lukas; Catucci, Damiano; Suter, Yannick; Hulbert, Leona; Berzigotti, Annalisa; Brönnimann, Michael; Ebner, Lukas; Christe, Andreas; Obmann, Verena Carola; Sznitman, Raphael; Huber, Adrian Thomas.
Afiliação
  • Zbinden L; ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland; Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland.
  • Catucci D; Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland; Graduate School for Health Sciences, University of Bern, Switzerland.
  • Suter Y; ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
  • Hulbert L; Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland.
  • Berzigotti A; Hepatology, Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, Bern, Switzerland.
  • Brönnimann M; Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland.
  • Ebner L; Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland.
  • Christe A; Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland.
  • Obmann VC; Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland.
  • Sznitman R; ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
  • Huber AT; Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland. Electronic address: adrian.huber@insel.ch.
Eur J Radiol ; 167: 111047, 2023 Oct.
Article em En | MEDLINE | ID: mdl-37690351
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Eur J Radiol Ano de publicação: 2023 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Eur J Radiol Ano de publicação: 2023 Tipo de documento: Article