Advanced Ultrasound in Diagnosis and Therapy ›› 2023, Vol. 7 ›› Issue (4): 333-347.doi: 10.37015/AUDT.2023.230016

• Review Articles • Previous Articles     Next Articles

Semi-supervised Learning for Real-time Segmentation of Ultrasound Video Objects: A Review

Jin Guo, MDa,1, Zhaojun Li, PhDb,1, Yanping Lin, PhDa,*()   

  1. aSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
    bDepartment of Ultrasound, Shanghai General Hospital Jiading Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
  • Received:2023-03-30 Revised:2023-04-07 Accepted:2023-04-22 Online:2023-12-30 Published:2023-10-23
  • Contact: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China. e-mail:,
  • About author:Jin Guo and Zhaojun Li have contributed equally to this study.


Real-time intelligent segmentation of ultrasound video object is a demanding task in the field of medical image processing and serves as an essential and critical step in image-guided clinical procedures. However, obtaining reliable and accurate medical image annotations often necessitates expert guidance, making the acquisition of large-scale annotated datasets challenging and costly. This presents obstacles for traditional supervised learning methods. Consequently, semi-supervised learning (SSL) has emerged as a promising solution, capable of utilizing unlabeled data to enhance model performance and has been widely adopted in medical image segmentation tasks. However, striking a balance between segmentation accuracy and inference speed remains a challenge for real-time segmentation. This paper provides a comprehensive review of research progress in real-time intelligent semi-supervised ultrasound video object segmentation (SUVOS) and offers insights into future developments in this area.

Key words: Ultrasound video segmentation; Semi-supervised learning; Real-time segmentation; Video object segmentation