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MITEA: A dataset for machine learning segmentation of the left ventricle in 3D echocardiography using subject-specific labels from cardiac magnetic resonance imaging.
Zhao, Debbie; Ferdian, Edward; Maso Talou, Gonzalo D; Quill, Gina M; Gilbert, Kathleen; Wang, Vicky Y; Babarenda Gamage, Thiranja P; Pedrosa, João; D'hooge, Jan; Sutton, Timothy M; Lowe, Boris S; Legget, Malcolm E; Ruygrok, Peter N; Doughty, Robert N; Camara, Oscar; Young, Alistair A; Nash, Martyn P.
Afiliación
  • Zhao D; Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
  • Ferdian E; Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.
  • Maso Talou GD; Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
  • Quill GM; Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
  • Gilbert K; Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
  • Wang VY; Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
  • Babarenda Gamage TP; Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
  • Pedrosa J; Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal.
  • D'hooge J; Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
  • Sutton TM; Counties Manukau Health Cardiology, Middlemore Hospital, Auckland, New Zealand.
  • Lowe BS; Green Lane Cardiovascular Service, Auckland City Hospital, Auckland, New Zealand.
  • Legget ME; Department of Medicine, University of Auckland, Auckland, New Zealand.
  • Ruygrok PN; Green Lane Cardiovascular Service, Auckland City Hospital, Auckland, New Zealand.
  • Doughty RN; Department of Medicine, University of Auckland, Auckland, New Zealand.
  • Camara O; Green Lane Cardiovascular Service, Auckland City Hospital, Auckland, New Zealand.
  • Young AA; Department of Medicine, University of Auckland, Auckland, New Zealand.
  • Nash MP; Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Front Cardiovasc Med ; 9: 1016703, 2022.
Article en En | MEDLINE | ID: mdl-36704465
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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: Front Cardiovasc Med Año: 2022 Tipo del documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: Front Cardiovasc Med Año: 2022 Tipo del documento: Article