Deep-learning prostate cancer detection and segmentation on biparametric versus multiparametric magnetic resonance imaging: Added value of dynamic contrast-enhanced imaging.

Journal: International journal of urology : official journal of the Japanese Urological Association
Published Date: Dec 1, 2023

Abstract

OBJECTIVES: To develop diagnostic algorithms of multisequence prostate magnetic resonance imaging for cancer detection and segmentation using deep learning and explore values of dynamic contrast-enhanced imaging in multiparametric imaging, compared with biparametric imaging.

Authors

  • Yoh Matsuoka
    Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan. yoh-m.uro@tmd.ac.jp.
  • Yoshihiko Ueno
    Department of Information and Communications Engineering, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan.
  • Sho Uehara
    Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan.
  • Hiroshi Tanaka
  • Masaki Kobayashi
    Mathematical Science Center, University of Yamanashi, Takeda 4-3-11, Kofu, Yamanashi 400-8511, Japan.
  • Hajime Tanaka
    Departments of 1Urology and.
  • Soichiro Yoshida
    Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan.
  • Minato Yokoyama
    Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan.
  • Itsuo Kumazawa
    Laboratory for Future, Interdisciplinary Research of Science and Technology, Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan.
  • Yasuhisa Fujii
    Department of Urology, Tokyo Medical and Dental University Graduate School, Tokyo, Japan. y-fujii.uro@tmd.ac.jp.

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