A 3D-2D Hybrid U-Net Convolutional Neural Network Approach to Prostate Organ Segmentation of Multiparametric MRI.

Journal: AJR. American journal of roentgenology
Published Date: Jan 1, 2021

Abstract

OBJECTIVE: Prostate cancer is the most commonly diagnosed cancer in men in the United States with more than 200,000 new cases in 2018. Multiparametric MRI (mpMRI) is increasingly used for prostate cancer evaluation. Prostate organ segmentation is an essential step of surgical planning for prostate fusion biopsies. Deep learning convolutional neural networks (CNNs) are the predominant method of machine learning for medical image recognition. In this study, we describe a deep learning approach, a subset of artificial intelligence, for automatic localization and segmentation of prostates from mpMRI.

Authors

  • Alexander Ushinsky
    Department of Radiological Sciences, University of California, Irvine, CA.
  • Michelle Bardis
    Center for Artificial Intelligence in Diagnostic Medicine, California Institute for Telecommunication and Information Technology, University of California, 4100 E Peltason Dr, Calit 2 Bldg, Ste 4500, Irvine, CA 92617.
  • Justin Glavis-Bloom
    Department of Radiological Sciences, University of California, Irvine, CA.
  • Edward Uchio
    Department of Urology, University of California, Irvine, CA.
  • Chanon Chantaduly
    Department of Radiological Sciences and Center for Artificial Intelligence in Diagnostic Medicine, University of California Irvine, Orange, California.
  • Michael Nguyentat
    Department of Radiology, University of Colorado Anschutz Medical Center, Aurora, CO.
  • Daniel Chow
    Department of Radiological Sciences, John Tu and Thomas Yuen Center for Functional Onco-Imaging, University of California, 164 Irvine Hall, Irvine, CA, 92697-5020.
  • Peter D Chang
    Department of Radiological Sciences and Center for Artificial Intelligence in Diagnostic Medicine, University of California Irvine, Orange, California.
  • Roozbeh Houshyar
    University of California Irvine, Radiology Department, UCI Medical Center, Orange, California, USA.

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