Automatic segmentation of pericardial adipose tissue from cardiac MR images via semi-supervised method with difference-guided consistency.

Journal: Medical physics
PMID: 39636531

Abstract

BACKGROUND: Accurate and automatic segmentation of pericardial adipose tissue (PEAT) in cardiac magnetic resonance (MR) images is essential for the diagnosis and treatment of cardiovascular diseases. Precise segmentation is challenging due to high costs and the need for specialized knowledge, as a large amount of accurately annotated data is required, demanding significant time and medical resources.

Authors

  • Xinru Zhang
    Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Abdominal Medical Imaging, Jinan, 250014, China.
  • Shoujun Zhou
    Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. Electronic address: sj.zhou@siat.ac.cn.
  • Bohan Li
    Department of Minimally Invasive Gynecologic Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing 100006, China.
  • Yuanquan Wang
    School of Artificial Intelligence, Hebei University of Technology (HeBUT), Tianjin, China.
  • Ke Lu
    University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China. Electronic address: luk@ucas.ac.cn.
  • Weipeng Liu
    College of Materials and Energy, South China Agricultural University, Guangzhou, 510642, China.
  • Zhida Wang
    NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, China.

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