Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps.

Journal: European radiology
Published Date: Jul 23, 2020

Abstract

OBJECTIVES: To evaluate short-term test-retest repeatability of a deep learning architecture (U-Net) in slice- and lesion-level detection and segmentation of clinically significant prostate cancer (csPCa: Gleason grade group > 1) using diffusion-weighted imaging fitted with monoexponential function, ADC.

Authors

  • Amogh Hiremath
    Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA. axh672@case.edu.
  • Rakesh Shiradkar
    Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.
  • Harri Merisaari
    Dept. of Diagnostic Radiology, University of Turku, Turku, Finland.
  • Prateek Prasanna
    Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, United States.
  • Otto Ettala
    Department of Urology, University of Turku and Turku University Hospital, Turku, Finland.
  • Pekka Taimen
    Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland.
  • Hannu J Aronen
    Department of Diagnostic Radiology, University of Turku, Turku, Finland.
  • Peter J Boström
    Department of Urology, Turku University Hospital, Turku, Finland.
  • Ivan Jambor
    Department of Diagnostic Radiology, University of Turku, Turku, Finland.
  • Anant Madabhushi
    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.

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