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Deep learning-based left ventricular segmentation demonstrates improved performance on respiratory motion-resolved whole-heart reconstructions.
Yang, Yitong; Shah, Zahraw; Jacob, Athira J; Hair, Jackson; Chitiboi, Teodora; Passerini, Tiziano; Yerly, Jerome; Di Sopra, Lorenzo; Piccini, Davide; Hosseini, Zahra; Sharma, Puneet; Sahu, Anurag; Stuber, Matthias; Oshinski, John N.
Afiliação
  • Yang Y; Wallace H. Coulter Department of Biomedical Engineering, Emory University and the Georgia Institute of Technology, Atlanta, GA, United States.
  • Shah Z; Wallace H. Coulter Department of Biomedical Engineering, Emory University and the Georgia Institute of Technology, Atlanta, GA, United States.
  • Jacob AJ; Digital Technology and Innovation, Siemens Medical Solutions USA, Princeton, NJ, United States.
  • Hair J; Wallace H. Coulter Department of Biomedical Engineering, Emory University and the Georgia Institute of Technology, Atlanta, GA, United States.
  • Chitiboi T; Digital Technology and Innovation, Siemens Medical Solutions USA, Princeton, NJ, United States.
  • Passerini T; Digital Technology and Innovation, Siemens Medical Solutions USA, Princeton, NJ, United States.
  • Yerly J; Diagnostic and Interventional Radiology, Lausanne University Hospital, Lausanne, Switzerland.
  • Di Sopra L; Diagnostic and Interventional Radiology, Lausanne University Hospital, Lausanne, Switzerland.
  • Piccini D; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.
  • Hosseini Z; MR R&D Collaboration, Siemens Medical Solutions USA, Atlanta, GA, United States.
  • Sharma P; Digital Technology and Innovation, Siemens Medical Solutions USA, Princeton, NJ, United States.
  • Sahu A; MR R&D Collaboration, Siemens Medical Solutions USA, Atlanta, GA, United States.
  • Stuber M; Diagnostic and Interventional Radiology, Lausanne University Hospital, Lausanne, Switzerland.
  • Oshinski JN; Wallace H. Coulter Department of Biomedical Engineering, Emory University and the Georgia Institute of Technology, Atlanta, GA, United States.
Front Radiol ; 3: 1144004, 2023.
Article em En | MEDLINE | ID: mdl-37492382
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Observational_studies Idioma: En Revista: Front Radiol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Observational_studies Idioma: En Revista: Front Radiol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos