A fully automatic deep learning-based method for segmenting regions of interest and predicting renal function in pediatric dynamic renal scintigraphy.

Journal: Annals of nuclear medicine
PMID: 38376629

Abstract

OBJECTIVE: Accurate delineation of renal regions of interest (ROIs) is critical for the assessment of renal function in pediatric dynamic renal scintigraphy (DRS). The purpose of this study was to develop and evaluate a deep learning (DL) model that can fully automatically delineate renal ROIs and calculate renal function in pediatric Technetium-ethylenedicysteine (Tc-EC) DRS.

Authors

  • Xueli Ji
    Department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
  • Guohui Zhu
    Institute of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200092, China.
  • Jinyu Gou
    Department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
  • Suyun Chen
    Department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
  • Wenyu Zhao
    Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), Shenzhen 518129, China.
  • Zhanquan Sun
    Institute of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200092, China. sunzhq@usst.edu.cn.
  • Hongliang Fu
    School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China.
  • Hui Wang
    Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.

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