Context-aware network fusing transformer and V-Net for semi-supervised segmentation of 3D left atrium

doi:10.1016/j.eswa.2022.119105

	Context-aware network fusing transformer and V-Net for semi-supervised segmentation of 3D left atrium
	Zhao, Chenji 1; Xiang, Shun 1; Wang, Yuanquan 1; Cai, Zhaoxi 2; Shen, Jun 2; Zhou, Shoujun 3; Zhao, Di 4; Su, Weihua 1; Guo, Shijie 1,5; Li, Shuo 6
	2023-03-15
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
卷号	214 页码:9
摘要	Accurate, robust and automatic segmentation of the left atrium (LA) in magnetic resonance images (MRI) is of great significance for studying the LA structure and facilitating the diagnosis and treatment of atrial fibrillation. Semi-supervised learning has attracted great attention in medical image segmentation, since it alleviates the heavy burden of annotating training data. In this paper, we propose a context-aware network called CA-Net for semi-supervised LA segmentation from 3D MRI. The information of 3D MRI to be learned is not only the contextual information in each slice, but also the spatial information among different slices of the data, which is not sufficiently exploit by existing methods. In the proposed CA-Net, a Trans-V module is coined from both Transformers and V-Net, which is able to learn contextual information in 3D MRI. In the training processing, the discriminator with attention mechanisms is introduced to calculate an adversarial loss so that a large amount of unlabeled data can be utilized. Experimental results on the Atrial Segmentation Challenge dataset show that the contextual information is helpful to extract more accurate atrial structures, and the proposed CA-Net achieves better performance than some SOTA semi-supervised networks. Our method achieves dice scores of 88.14% and 90.09% in segmentation results when trained with 10% and 20% of labeled data, respectively. Code will be available at: https://github.com/RhythmI/CA-Net-master.
关键词	3D MRI Contextual information Image segmentation Semi -supervised learning Transformers
DOI	10.1016/j.eswa.2022.119105
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFA0704102] ; National Science Foundation of China (NSFC)[61976241] ; National Science Foundation of China (NSFC)[81827805] ; National Science Foundation of China (NSFC)[61871173] ; National Science Foundation of China (NSFC)[91948303] ; Basic Research Project of Shenzhen Science and Technology Innovation Commission[JCYJ20200109114610201] ; Basic Research Project of Shenzhen Science and Technology Innovation Commission[JCYJ20200109114812361] ; Shenzhen Engineering Laboratory for Interventional Key Technology of Diagnosis and Treatment with Surgical Robot ; Tianjin Science and Technology Planning Project[19ZXJRGX00080]
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:000883786000011
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：41[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/2XEOYT63/19851
专题	中国科学院计算技术研究所期刊论文
通讯作者	Wang, Yuanquan; Zhou, Shoujun
作者单位	1.Hebei Univ Technol HeBUT, Sch Artificial Intelligence, Tianjin 300401, Peoples R China 2.Sun Yat Sen Univ, Sun Yat Sen Mem Hosp, Dept Radiol, Guangzhou 510120, Peoples R China 3.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 5.HeBUT, Hebei Key Lab Robot Percept & Human Robot Interact, Tianjin 300401, Peoples R China 6.Western Univ, Dept Med Imaging, DIG London, London, ON N6A 4V2, Canada
推荐引用方式 GB/T 7714	Zhao, Chenji,Xiang, Shun,Wang, Yuanquan,et al. Context-aware network fusing transformer and V-Net for semi-supervised segmentation of 3D left atrium[J]. EXPERT SYSTEMS WITH APPLICATIONS,2023,214:9.
APA	Zhao, Chenji.,Xiang, Shun.,Wang, Yuanquan.,Cai, Zhaoxi.,Shen, Jun.,...&Li, Shuo.(2023).Context-aware network fusing transformer and V-Net for semi-supervised segmentation of 3D left atrium.EXPERT SYSTEMS WITH APPLICATIONS,214,9.
MLA	Zhao, Chenji,et al."Context-aware network fusing transformer and V-Net for semi-supervised segmentation of 3D left atrium".EXPERT SYSTEMS WITH APPLICATIONS 214(2023):9.