Deep Learning Improves Speed and Accuracy of Prostate Gland Segmentations on Magnetic Resonance Imaging for Targeted Biopsy.

Journal: The Journal of urology
Published Date: Sep 1, 2021

Abstract

PURPOSE: Targeted biopsy improves prostate cancer diagnosis. Accurate prostate segmentation on magnetic resonance imaging (MRI) is critical for accurate biopsy. Manual gland segmentation is tedious and time-consuming. We sought to develop a deep learning model to rapidly and accurately segment the prostate on MRI and to implement it as part of routine magnetic resonance-ultrasound fusion biopsy in the clinic.

Authors

  • Simon John Christoph Soerensen
    Department of Urology, Stanford University School of Medicine, Stanford, California.
  • Richard E Fan
    Department of Urology, Stanford University, Stanford, CA 94305, USA.
  • Arun Seetharaman
    Department of Electrical Engineering, Stanford University, Stanford, California.
  • Leo Chen
    Cancer Strategy & Capital Redevelopment, British Columbia Cancer Agency, Vancouver, British Columbia, Canada.
  • Wei Shao
  • Indrani Bhattacharya
    Department of Radiology, Stanford University School of Medicine, Stanford, California.
  • Yong-Hun Kim
  • Rewa Sood
    Department of Electrical Engineering, Stanford University, Stanford, California.
  • Michael Borre
    Department of Urology, Aarhus University Hospital, Aarhus, Denmark.
  • Benjamin I Chung
    Department of Urology, Stanford University School of Medicine, Stanford, CA, USA.
  • Katherine J To'o
    Department of Radiology, Stanford University School of Medicine, Stanford, California.
  • Mirabela Rusu
    Department of Radiology, Stanford University, Stanford, CA 94305, USA. Electronic address: mirabela.rusu@stanford.edu.
  • Geoffrey A Sonn
    Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA; Department of Urology, Stanford University School of Medicine, Stanford, CA, USA.

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