Feasibility of a deep learning-based method for automated localization of pelvic floor landmarks using stress MR images.

Journal: International urogynecology journal
Published Date: Jan 21, 2021

Abstract

INTRODUCTION AND HYPOTHESIS: Magnetic resonance imaging (MRI) plays an important role in assessing pelvic organ prolapse (POP), and automated pelvic floor landmark localization potentially accelerates MRI-based measurements of POP. Herein, we aimed to develop and evaluate a deep learning-based technique for automated localization of POP-related landmarks.

Authors

  • Fei Feng
    Department of Mathematical, Yunnan Normal University, Kunming 650092, People's Republic of China.
  • James A Ashton-Miller
    Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
  • John O L DeLancey
    Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Jiajia Luo
    Biomedical Engineering Department, Peking University, Beijing, 100191, China. jiajia.luo@pku.edu.cn.

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