干旱区生态环境知识资源中心(Arid): Affine medical image registration with fusion feature mapping in local and global

Arid

DOI	10.1088/1361-6560/ad2717
	Affine medical image registration with fusion feature mapping in local and global
	Ji, Wei; Yang, Feng
通讯作者	Yang, F
来源期刊	PHYSICS IN MEDICINE AND BIOLOGY
ISSN	0031-9155
EISSN	1361-6560
出版年	2024
卷号	69 期号:5
英文摘要	Objective. Medical image affine registration is a crucial basis before using deformable registration. On the one hand, the traditional affine registration methods based on step-by-step optimization are very time-consuming, so these methods are not compatible with most real-time medical applications. On the other hand, convolutional neural networks are limited in modeling long-range spatial relationships of the features due to inductive biases, such as weight sharing and locality. This is not conducive to affine registration tasks. Therefore, the evolution of real-time and high-accuracy affine medical image registration algorithms is necessary for registration applications. Approach. In this paper, we propose a deep learning-based coarse-to-fine global and local feature fusion architecture for fast affine registration, and we use an unsupervised approach for end-to-end training. We use multiscale convolutional kernels as our elemental convolutional blocks to enhance feature extraction. Then, to learn the long-range spatial relationships of the features, we propose a new affine registration framework with weighted global positional attention that fuses global feature mapping and local feature mapping. Moreover, the fusion regressor is designed to generate the affine parameters. Main results. The additive fusion method can be adaptive to global mapping and local mapping, which improves affine registration accuracy without the center of mass initialization. In addition, the max pooling layer and the multiscale convolutional kernel coding module increase the ability of the model in affine registration. Significance. We validate the effectiveness of our method on the OASIS dataset with 414 3D MRI brain maps. Comprehensive results demonstrate that our method achieves state-of-the-art affine registration accuracy and very efficient runtimes.
英文关键词	medical image registration affine registration multiscale convolutional kernel weighted global positional attention unsupervised learning fusion regressor
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001175157400001
WOS关键词	FRAMEWORK ; MRI
WOS类目	Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向	Engineering ; 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/2XILL650/405062
推荐引用方式 GB/T 7714	Ji, Wei,Yang, Feng. Affine medical image registration with fusion feature mapping in local and global[J],2024,69(5).
APA	Ji, Wei,&Yang, Feng.(2024).Affine medical image registration with fusion feature mapping in local and global.PHYSICS IN MEDICINE AND BIOLOGY,69(5).
MLA	Ji, Wei,et al."Affine medical image registration with fusion feature mapping in local and global".PHYSICS IN MEDICINE AND BIOLOGY 69.5(2024).