A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: To determine the value of a deep learning masked (DLM) auto-fixed volume of interest (VOI) segmentation method as an alternative to manual segmentation for radiomics-based diagnosis of clinically significant (CS) prostate cancer (PCa) on biparametric magnetic resonance imaging (bpMRI).

Authors

  • Jeroen Bleker
    Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Meditech Building, Room 305, Hanzeplein 1, 9700 RB, Groningen, The Netherlands. j.bleker@umcg.nl.
  • Thomas C Kwee
    Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  • Dennis Rouw
    Department of Radiology, Martini Hospital Groningen, Van Swietenplein 1, 9728 NT, Groningen, The Netherlands.
  • Christian Roest
    Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Meditech Building, Room 305, Hanzeplein 1, 9700 RB, Groningen, The Netherlands.
  • Jaap Borstlap
    Department of Radiology, Treant Zorggroep, Dr. G.H. Amshoffweg 1, 7909 AA, Hoogeveen, The Netherlands.
  • Igle Jan de Jong
    Department of Urology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB, Groningen, The Netherlands.
  • Rudi A J O Dierckx
    Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700 RB, Groningen, The Netherlands.
  • Henkjan Huisman
    Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Derya Yakar
    Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. Electronic address: d.yakar@umcg.nl.