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A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Patients With Cerebrovascular Disease.
Livne, Michelle; Rieger, Jana; Aydin, Orhun Utku; Taha, Abdel Aziz; Akay, Ela Marie; Kossen, Tabea; Sobesky, Jan; Kelleher, John D; Hildebrand, Kristian; Frey, Dietmar; Madai, Vince I.
Afiliación
  • Livne M; Predictive Modelling in Medicine Research Group, Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • Rieger J; Centre for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • Aydin OU; Predictive Modelling in Medicine Research Group, Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • Taha AA; Predictive Modelling in Medicine Research Group, Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • Akay EM; Research Studios Data Science, Research Studios Austria, Salzburg, Austria.
  • Kossen T; Predictive Modelling in Medicine Research Group, Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • Sobesky J; Predictive Modelling in Medicine Research Group, Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • Kelleher JD; Centre for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • Hildebrand K; Centre for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • Frey D; Department of Neurology, Johanna-Etienne Hospital Neuss, Neuss, Germany.
  • Madai VI; Information, Communication and Entertainment Institute (ICE), Dublin Institute of Technology, Dublin, Ireland.
Front Neurosci ; 13: 97, 2019.
Article en En | MEDLINE | ID: mdl-30872986
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Qualitative_research Idioma: En Revista: Front Neurosci Año: 2019 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Qualitative_research Idioma: En Revista: Front Neurosci Año: 2019 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Suiza