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Deep learning-based stenosis quantification from coronary CT Angiography.
Hong, Youngtaek; Commandeur, Frederic; Cadet, Sebastien; Goeller, Markus; Doris, Mhairi K; Chen, Xi; Kwiecinski, Jacek; Berman, Daniel S; Slomka, Piotr J; Chang, Hyuk-Jae; Dey, Damini.
Afiliação
  • Hong Y; Brain Korea 21 Project for Medical Science, Yonsei University, Seoul, Republic of Korea.
  • Commandeur F; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Cadet S; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Goeller M; Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Doris MK; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Chen X; Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Faculty of Medicine, Department of Cardiology, Erlangen, Germany.
  • Kwiecinski J; Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Berman DS; Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom.
  • Slomka PJ; Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Chang HJ; Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Dey D; Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom.
Proc SPIE Int Soc Opt Eng ; 109492019 Feb.
Article em En | MEDLINE | ID: mdl-31762536
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article
Texto completo
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PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article