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Automatic Deep-Learning Segmentation of Epicardial Adipose Tissue from Low-Dose Chest CT and Prognosis Impact on COVID-19.
Bartoli, Axel; Fournel, Joris; Ait-Yahia, Léa; Cadour, Farah; Tradi, Farouk; Ghattas, Badih; Cortaredona, Sébastien; Million, Matthieu; Lasbleiz, Adèle; Dutour, Anne; Gaborit, Bénédicte; Jacquier, Alexis.
Afiliação
  • Bartoli A; Department of Radiology, Hôpital de la TIMONE, AP-HM, 13005 Marseille, France.
  • Fournel J; CRMBM-UMR CNRS 7339, Aix-Marseille University, 27, Boulevard Jean Moulin, 13005 Marseille, France.
  • Ait-Yahia L; CRMBM-UMR CNRS 7339, Aix-Marseille University, 27, Boulevard Jean Moulin, 13005 Marseille, France.
  • Cadour F; Department of Radiology, Hôpital de la TIMONE, AP-HM, 13005 Marseille, France.
  • Tradi F; Department of Radiology, Hôpital de la TIMONE, AP-HM, 13005 Marseille, France.
  • Ghattas B; CRMBM-UMR CNRS 7339, Aix-Marseille University, 27, Boulevard Jean Moulin, 13005 Marseille, France.
  • Cortaredona S; Department of Radiology, Hôpital de la TIMONE, AP-HM, 13005 Marseille, France.
  • Million M; I2M-UMR CNRS 7373, Luminy Faculty of Sciences, Aix-Marseille University, 163 Avenue de Luminy, Case 901, 13009 Marseille, France.
  • Lasbleiz A; IHU Méditerranée Infection, 19-21 Boulevard Jean Moulin, 13005 Marseille, France.
  • Dutour A; VITROME, SSA, IRD, Aix-Marseille University, 13005 Marseille, France.
  • Gaborit B; IHU Méditerranée Infection, 19-21 Boulevard Jean Moulin, 13005 Marseille, France.
  • Jacquier A; MEPHI, IRD, AP-HM, Aix Marseille University, 13005 Marseille, France.
Cells ; 11(6)2022 03 18.
Article em En | MEDLINE | ID: mdl-35326485
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / COVID-19 Tipo de estudo: Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Cells Ano de publicação: 2022 Tipo de documento: Article País de afiliação: França
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / COVID-19 Tipo de estudo: Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Cells Ano de publicação: 2022 Tipo de documento: Article País de afiliação: França