A Bi-modal Temporal Segmentation Network for Automated Segmentation of Focal Liver Lesions in Dynamic Contrast-enhanced Ultrasound.

Journal: Ultrasound in medicine & biology
PMID:

Abstract

OBJECTIVE: To develop and validate an automated deep learning-based model for focal liver lesion (FLL) segmentation in a dynamic contrast-enhanced ultrasound (CEUS) video.

Authors

  • Yu Duan
    Yunnan Characteristic Plant Extraction Laboratory Co., Ltd., Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education and Yunnan Province, School of Chemical Science and Technology, Yunnan University, Kunming, 650500, China.
  • Siyuan Shi
    Department of Ultrasound, The Second Clinical Medical College,Jinan University, Guangdong, China.
  • Haiyi Long
    Department of Medical Ultrasonics, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Xian Zhong
    The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.
  • Yang Tan
    Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.
  • Guangjian Liu
    Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
  • Guanghua Wu
    Department of Medical Ultrasonics, SanMing First Hospital, Sanming, China.
  • Si Qin
    Department of Medical Ultrasonics, Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Xiaoyan Xie
    Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
  • Manxia Lin
    Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China. linmxia@mail.sysu.edu.cn.