Quantitative measurement of the ureter on three-dimensional magnetic resonance urography images using deep learning.

Journal: Medical physics
PMID: 38477634

Abstract

BACKGROUND: Accurate measurement of ureteral diameters plays a pivotal role in diagnosing and monitoring urinary tract obstruction (UTO). While three-dimensional magnetic resonance urography (3D MRU) represents a significant advancement in imaging, the traditional manual methods for assessing ureteral diameters are characterized by labor-intensive procedures and inherent variability. In the realm of medical image analysis, deep learning has led to a paradigm shift, yet the development of a comprehensive automated tool for the precise segmentation and measurement of ureters in MR images is an unaddressed challenge.

Authors

  • Rile Nai
    Department of Radiology, 26447Peking University First Hospital, Beijing, PR China.
  • Kexin Wang
    Clifford Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Xiaoqing Li
  • Shangsong Du
    Department of Radiology, Peking University First Hospital, Beijing, China.
  • Tuya E
    Department of Radiology, 26447Peking University First Hospital, Beijing, PR China.
  • He Xiao
    Department of Radiology, Beijing Changping Hospital, Beijing, China.
  • Shuo Quan
    Department of Radiology, Peking University First Hospital, Beijing, China.
  • Yaofeng Zhang
    Beijing Smart Tree Medical Technology co. Ltd., Beijing, China.
  • Junhua Yu
    Beijing Smart Tree Medical Technology Co. Ltd., No.24 Huangsi Street, Xicheng District, Beijing, 100011, China.
  • Jialun Li
    Beijing Smart Tree Medical Technology Co. Ltd., Beijing, 100011, China.
  • Xiaodong Zhang
    The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • XiaoYing Wang

Keywords

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