A deep learning-based self-adapting ensemble method for segmentation in gynecological brachytherapy.

Journal: Radiation oncology (London, England)
Published Date: Sep 5, 2022

Abstract

PURPOSE: Fast and accurate outlining of the organs at risk (OARs) and high-risk clinical tumor volume (HRCTV) is especially important in high-dose-rate brachytherapy due to the highly time-intensive online treatment planning process and the high dose gradient around the HRCTV. This study aims to apply a self-configured ensemble method for fast and reproducible auto-segmentation of OARs and HRCTVs in gynecological cancer.

Authors

  • Zhen Li
    PepsiCo R&D, Valhalla, NY, United States.
  • Qingyuan Zhu
    Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Xuhui District, Shanghai, China.
  • Lihua Zhang
    Department of Mathematics, University of California, Irvine, CA, 92697, USA.
  • Xiaojing Yang
    Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Xuhui District, Shanghai, China.
  • Zhaobin Li
    Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Xuhui District, Shanghai, China. zlb_2@163.com.
  • Jie Fu
    David Geffen School of Medicine, University of California, Los Angeles, 10833 Le Conte Ave, Los Angeles, 90095, CA, USA.

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