Review of Unsupervised Domain Adaptation in Medical Image Segmentation

被引：2

作者：

Hu, Wei ^{[1
]}

Xu, Qiaozhi ^{[1
]}

Ge, Xiangwei ^{[1
]}

Yu, Lei ^{[2
]}

机构：

[1] College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot,010022, China

[2] Inner Mongolia Autonomous Region People’s Hospital, Hohhot,010020, China

来源：

Computer Engineering and Applications | 2024年 / 60卷 / 06期

关键词：

Image segmentation - Medical image processing;

D O I：

10.3778/j.issn.1002-8331.2307-0421

中图分类号：

学科分类号：

摘要：

Medical image segmentation has broad application prospects in the field of medical image processing, providing auxiliary information for diagnosis and treatment by locating and segmenting interested organs, tissues, or lesion areas. However, there is a domain offset problem between different modalities of medical images, which can lead to a significant decrease in the performance of the segmentation model during actual deployment. Domain adaptation technology is an effective way to solve this problem, especially unsupervised domain adaptation, which has become a research hotspot in the field of medical image processing because it does not require target domain label information. At present, there are relatively few review reports on unsupervised domain adaptation research in medical image segmentation. Therefore, this paper summarizes, analyzes, and prospects the future of unsupervised domain adaptation research in medical image segmentation in recent years, hoping to help relevant researchers quickly understand and familiarize themselves with the current research status and trends in this field. © 2024 Editorial Department of Scientia Agricultura Sinica. All rights reserved.

引用

页码：10 / 26