LE-UDA: Label-Efficient Unsupervised Domain Adaptation for Medical Image Segmentation

doi:10.1109/TMI.2022.3214766

	LE-UDA: Label-Efficient Unsupervised Domain Adaptation for Medical Image Segmentation
	Zhao, Ziyuan 1,2,3; Zhou, Fangcheng 4,5; Xu, Kaixin 2; Zeng, Zeng 2,6; Guan, Cuntai 1; Zhou, S. Kevin 7,8
	2023-03-01
发表期刊	IEEE TRANSACTIONS ON MEDICAL IMAGING
ISSN	0278-0062
卷号	42 期号:3 页码:633-646
摘要	While deep learning methods hitherto have achieved considerable success in medical image segmentation, they are still hampered by two limitations: (i) reliance on large-scale well-labeled datasets, which are difficult to curate due to the expert-driven and time-consuming nature of pixel-level annotations in clinical practices, and (ii) failure to generalize from one domain to another, especially when the target domain is a different modality with severe domain shifts. Recent unsupervised domain adaptation (UDA) techniques leverage abundant labeled source data together with unlabeled target data to reduce the domain gap, but these methods degrade significantly with limited source annotations. In this study, we address this underexplored UDA problem, investigating a challenging but valuable realistic scenario, where the source domain not only exhibits domain shift w.r.t. the target domain but also suffers from label scarcity. In this regard, we propose a novel and generic framework called "Label-Efficient Unsupervised Domain Adaptation " (LE-UDA). In LE-UDA, we construct self-ensembling consistency for knowledge transfer between both domains, as well as a self-ensembling adversarial learning module to achieve better feature alignment for UDA. To assess the effectiveness of our method, we conduct extensive experiments on two different tasks for cross-modality segmentation between MRI and CT images. Experimental results demonstrate that the proposed LE-UDA can efficiently leverage limited source labels to improve cross-domain segmentation performance, outperforming state-of-the-art UDA approaches in the literature.
关键词	Image segmentation Adaptation models Biomedical imaging Annotations Adversarial machine learning Magnetic resonance imaging Training Unsupervised domain adaptation medical image segmentation cross-modality learning semi-supervised learning adversarial learning
DOI	10.1109/TMI.2022.3214766
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000971629600006
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：18[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21458
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zeng, Zeng; Guan, Cuntai
作者单位	1.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 100190, Singapore 2.ASTAR, Inst Infocomm Res I2R, Singapore 138632, Singapore 3.ASTAR, Artificial Intelligence Analyt & Informat AI3, Singapore 138632, Singapore 4.ASTAR, I2R, Singapore, Singapore 5.Natl Univ Singapore, Dept Math, Singapore 119076, Singapore 6.Shanghai Univ, Sch Microelect, Shanghai 200444, Peoples R China 7.Univ Sci & Technol China, Suzhou Inst Adv Res, Sch Biomed Engn, Ctr Med Imaging Robot Analyt Computing & Learning, Suzhou 215123, Peoples R China 8.Chinese Acad Sci, Inst Comp Technol, CAS, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Ziyuan,Zhou, Fangcheng,Xu, Kaixin,et al. LE-UDA: Label-Efficient Unsupervised Domain Adaptation for Medical Image Segmentation[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2023,42(3):633-646.
APA	Zhao, Ziyuan,Zhou, Fangcheng,Xu, Kaixin,Zeng, Zeng,Guan, Cuntai,&Zhou, S. Kevin.(2023).LE-UDA: Label-Efficient Unsupervised Domain Adaptation for Medical Image Segmentation.IEEE TRANSACTIONS ON MEDICAL IMAGING,42(3),633-646.
MLA	Zhao, Ziyuan,et al."LE-UDA: Label-Efficient Unsupervised Domain Adaptation for Medical Image Segmentation".IEEE TRANSACTIONS ON MEDICAL IMAGING 42.3(2023):633-646.