Deep learning applications in automatic needle segmentation in ultrasound-guided prostate brachytherapy.

Journal: Medical physics
Published Date: Jul 16, 2020

Abstract

PURPOSE: High-Dose-Rate (HDR) brachytherapy is one of the most effective ways to treat the prostate cancer, which is the second most common cancer in men worldwide. This treatment delivers highly conformal dose through the transperineal needle implants and is guided by a real time ultrasound (US) imaging system. Currently, the brachytherapy needles in the US images are manually segmented by physicists during the treatment, which is time consuming and error prone. In this study, we propose a set of deep learning-based algorithms to accurately segment the brachytherapy needles and locate the needle tips from the US images.

Authors

  • Fuyue Wang
    Department of Engineering Physics, Tsinghua University, Beijing, 100084, China.
  • Lei Xing
    Department of Radiation Oncology, Stanford University, CA, USA.
  • Hilary Bagshaw
    Stanford University, Department of Radiation Oncology, Stanford, USA. Electronic address: hbagshaw@stanford.edu.
  • Mark Buyyounouski
    Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China.
  • Bin Han
    2 Department of Radiation Oncology, Stanford University, Stanford, CA, USA.

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