@article{Guo2023, 
author = {Jin Guo and Zhaojun Li and Yanping Lin},
title = {Semi-supervised Learning for Real-time Segmentation of Ultrasound Video Objects: A Review},
year = {2023},
journal = {Advanced Ultrasound in Diagnosis and Therapy},
volume = {7},
number = {4},
pages = {333-347},
keywords = {Ultrasound video segmentation, Semi-supervised learning, Real-time segmentation, Video object segmentation},
url = {https://www.sciopen.com/article/10.37015/AUDT.2023.230016},
doi = {10.37015/AUDT.2023.230016},
abstract = {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.}
}