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A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT.
Arab, Ali; Chinda, Betty; Medvedev, George; Siu, William; Guo, Hui; Gu, Tao; Moreno, Sylvain; Hamarneh, Ghassan; Ester, Martin; Song, Xiaowei.
Afiliación
  • Arab A; School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.
  • Chinda B; Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada.
  • Medvedev G; Health Sciences and Innovation, Surrey Memorial Hospital, Fraser Health Authority, Surrey, BC, Canada.
  • Siu W; Division of Neurology, Royal Columbian Hospital, New Westminster, BC, Canada.
  • Guo H; Division of Radiology, Royal Columbian Hospital, New Westminster, BC, Canada.
  • Gu T; Health Sciences and Innovation, Surrey Memorial Hospital, Fraser Health Authority, Surrey, BC, Canada.
  • Moreno S; Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China.
  • Hamarneh G; Health Sciences and Innovation, Surrey Memorial Hospital, Fraser Health Authority, Surrey, BC, Canada.
  • Ester M; Department of Radiology, Beijing Hospital, Beijing, China.
  • Song X; School of Interactive Arts and Technology, Simon Fraser University, Surrey, BC, Canada.
Sci Rep ; 10(1): 19389, 2020 11 09.
Article en En | MEDLINE | ID: mdl-33168895
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X / Hemorragias Intracraneales / Aprendizaje Profundo / Cabeza Tipo de estudio: Prognostic_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Sci Rep Año: 2020 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
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XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X / Hemorragias Intracraneales / Aprendizaje Profundo / Cabeza Tipo de estudio: Prognostic_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Sci Rep Año: 2020 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Reino Unido