Automated detection and segmentation of pleural effusion on ultrasound images using an Attention U-net.

Journal: Journal of applied clinical medical physics
Published Date: Dec 13, 2023

Abstract

BACKGROUND: Ultrasonic for detecting and evaluating pleural effusion is an essential part of the Extended Focused Assessment with Sonography in Trauma (E-FAST) in emergencies. Our study aimed to develop an Artificial Intelligence (AI) diagnostic model that automatically identifies and segments pleural effusion areas on ultrasonography.

Authors

  • Libing Huang
    Department of Ultrasound, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China.
  • Yingying Lin
    Department of Center of Integrated Traditional Chinese and Western Medicine, Peking University Ditan Teaching Hospital, Beijing, People's Republic of China.
  • Peng Cao
    Medical Image Computing Laboratory of Ministry of Education, Northeastern University, 110819, Shenyang, China.
  • Xia Zou
    Department of Ultrasound, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China.
  • Qian Qin
    Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai, China.
  • Zhanye Lin
    Shantou University Medical College, Shantou, China.
  • Fengting Liang
    Department of Ultrasound, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China.
  • Zhengyi Li
    Department of Ultrasound, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China.

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