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Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps.
Hiremath, Amogh; Shiradkar, Rakesh; Merisaari, Harri; Prasanna, Prateek; Ettala, Otto; Taimen, Pekka; Aronen, Hannu J; Boström, Peter J; Jambor, Ivan; Madabhushi, Anant.
Affiliation
  • Hiremath A; Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA. axh672@case.edu.
  • Shiradkar R; Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.
  • Merisaari H; Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.
  • Prasanna P; Department of Diagnostic Radiology, University of Turku, Turku, Finland.
  • Ettala O; Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.
  • Taimen P; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.
  • Aronen HJ; Department of Urology, University of Turku and Turku University Hospital, Turku, Finland.
  • Boström PJ; Institute of Biomedicine, Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland.
  • Jambor I; Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland.
  • Madabhushi A; Department of Urology, University of Turku and Turku University Hospital, Turku, Finland.
Eur Radiol ; 31(1): 379-391, 2021 Jan.
Article in En | MEDLINE | ID: mdl-32700021
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Prostatic Neoplasms / Deep Learning Type of study: Prognostic_studies Limits: Humans / Male Language: En Journal: Eur Radiol Journal subject: RADIOLOGIA Year: 2021 Document type: Article Affiliation country: United States Country of publication: Germany
Fulltext
Add to My VHL
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Prostatic Neoplasms / Deep Learning Type of study: Prognostic_studies Limits: Humans / Male Language: En Journal: Eur Radiol Journal subject: RADIOLOGIA Year: 2021 Document type: Article Affiliation country: United States Country of publication: Germany