干旱区生态环境知识资源中心(Arid): TransMatch: A Transformer-Based Multilevel Dual-Stream Feature Matching Network for Unsupervised Deformable Image Registration

Arid

DOI	10.1109/TMI.2023.3288136
	TransMatch: A Transformer-Based Multilevel Dual-Stream Feature Matching Network for Unsupervised Deformable Image Registration
	Chen, Zeyuan; Zheng, Yuanjie; Gee, James C.
通讯作者	Zheng, YJ
来源期刊	IEEE TRANSACTIONS ON MEDICAL IMAGING
ISSN	0278-0062
EISSN	1558-254X
出版年	2024
卷号	43 期号:1 页码:15-27
英文摘要	Feature matching, which refers to establishing the correspondence of regions between two images (usually voxel features), is a crucial prerequisite of feature-based registration. For deformable image registration tasks, traditional feature-based registration methods typically use an iterative matching strategy for interest region matching, where feature selection and matching are explicit, but specific feature selection schemes are often useful in solving application-specific problems and require several minutes for each registration. In the past few years, the feasibility of learning-based methods, such as VoxelMorph and TransMorph, has been proven, and their performance has been shown to be competitive compared to traditional methods. However, these methods are usually single-stream, where the two images to be registered are concatenated into a 2-channel whole, and then the deformation field is output directly. The transformation of image features into interimage matching relationships is implicit. In this paper, we propose a novel end-to-end dual-stream unsupervised framework, named TransMatch, where each image is fed into a separate stream branch, and each branch performs feature extraction independently. Then, we implement explicit multilevel feature matching between image pairs via the query-key matching idea of the self-attention mechanism in the Transformer model. Comprehensive experiments are conducted on three 3D brain MR datasets, LPBA40, IXI, and OASIS, and the results show that the proposed method achieves state-of-the-art performance in several evaluation metrics compared to the commonly utilized registration methods, including SyN, NiftyReg, VoxelMorph, CycleMorph, ViT-V-Net, and TransMorph, demonstrating the effectiveness of our model in deformable medical image registration.
英文关键词	Deformable image registration feature matching transformer dual-stream multilevel unsupervised deep learning brain MRI
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001158081600006
WOS关键词	FRAMEWORK ; HAMMER
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向	Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404152
推荐引用方式 GB/T 7714	Chen, Zeyuan,Zheng, Yuanjie,Gee, James C.. TransMatch: A Transformer-Based Multilevel Dual-Stream Feature Matching Network for Unsupervised Deformable Image Registration[J],2024,43(1):15-27.
APA	Chen, Zeyuan,Zheng, Yuanjie,&Gee, James C..(2024).TransMatch: A Transformer-Based Multilevel Dual-Stream Feature Matching Network for Unsupervised Deformable Image Registration.IEEE TRANSACTIONS ON MEDICAL IMAGING,43(1),15-27.
MLA	Chen, Zeyuan,et al."TransMatch: A Transformer-Based Multilevel Dual-Stream Feature Matching Network for Unsupervised Deformable Image Registration".IEEE TRANSACTIONS ON MEDICAL IMAGING 43.1(2024):15-27.