干旱区生态环境知识资源中心(Arid): Feature self-calibration network with global-local training strategy for multi-region deformable medical image registration

Arid

DOI	10.1007/s00521-022-07365-4
	Feature self-calibration network with global-local training strategy for multi-region deformable medical image registration
	Zheng, Zhiyuan; Cao, Wenming; Lian, Deliang; Luo, Yi
通讯作者	Cao, WM
来源期刊	NEURAL COMPUTING & APPLICATIONS
ISSN	0941-0643
EISSN	1433-3058
出版年	2022
卷号	34 期号:19 页码:17175-17191
英文摘要	3D deformable medical image registration has important clinical significance. Deep learning-based methods have shown outstanding advantages in medical image registration. However, most current learning-based practices only have sound registration effects for large-area overall deformed medical images such as lung and liver CT scans. Medical images with multiple registration regions, such as brain MR scans, have become a significant shortcoming in the field of medical image registration. The current learning-based method uses only one displacement field to deform the entire moving image. For brain images with multiple registration areas, this method cannot guarantee accurate deformation of all regions, nor can it ensure the excellent maintenance of the topological structure of each part. Aiming to resolve this unresolved problem in the field of medical image registration, we propose a novel training strategy suitable for multi-region registration: global and local joint training strategy (GoLo). And then, we combine it with our proposed feature self-calibration network (FSCN) to solve the registration optimization problem of multi-region medical images. We conducted many quantitative and qualitative evaluation tests on the public brain MR scan data set(OASIS). The test results show that our method can further improve the accuracy of the registered image and ensure reversibility compared with the existing state-of-the-art methods.
英文关键词	Multi-region medical image registration Global and local joint training strategy Feature self-calibration network
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000802154700003
WOS关键词	FRAMEWORK
WOS类目	Computer Science, Artificial Intelligence
WOS研究方向	Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393859
推荐引用方式 GB/T 7714	Zheng, Zhiyuan,Cao, Wenming,Lian, Deliang,et al. Feature self-calibration network with global-local training strategy for multi-region deformable medical image registration[J],2022,34(19):17175-17191.
APA	Zheng, Zhiyuan,Cao, Wenming,Lian, Deliang,&Luo, Yi.(2022).Feature self-calibration network with global-local training strategy for multi-region deformable medical image registration.NEURAL COMPUTING & APPLICATIONS,34(19),17175-17191.
MLA	Zheng, Zhiyuan,et al."Feature self-calibration network with global-local training strategy for multi-region deformable medical image registration".NEURAL COMPUTING & APPLICATIONS 34.19(2022):17175-17191.