干旱区生态环境知识资源中心(Arid): A light-weight rectangular decomposition large kernel convolution network for deformable medical image registration

Arid

DOI	10.1016/j.bspc.2024.106476
	A light-weight rectangular decomposition large kernel convolution network for deformable medical image registration
	Cao, Yuzhu; Cao, Weiwei; Wang, Ziyu; Yuan, Gang; Li, Zeyi; Ni, Xinye; Zheng, Jian
通讯作者	Zheng, J
来源期刊	BIOMEDICAL SIGNAL PROCESSING AND CONTROL
ISSN	1746-8094
EISSN	1746-8108
出版年	2024
卷号	95
英文摘要	The performance and speed of medical image registration have been greatly boosted by advanced deeplearning based methods. However, most current methods are challenged by large deformations between input images, which necessitate a compromise in computational cost to enhance the model's receptive field and its ability to model long-range spatial relationships for improving registration performance. In order to enhance the performance of registration for images with large deformations at a lower computational cost, in this paper, we propose a light -weight registration model with the ability to model large receptive fields and long-range spatial relationships, named LL -Net. The core components of LL -Net consist of a Rectangular Decomposition Large Kernel Attention (RD-LKA) layer and a Spatial and Channel Fusion Attention (SC -Fusion) layer. The RD-LKA layer utilizes anisotropic depth -wise large kernel convolutions to capture large receptive fields with an extremely low parameter count while modeling long-range spatial relationships. Moreover, the SC -Fusion layer enhances the model's feature fusion capability and strengthens feature representations at critical locations. Our LL -Net exhibits state-of-the-art performance across multiple datasets. Specifically, it achieves a Dice score of 76.7% and an HD95 of 2.983 mm on the IXI dataset, and a Dice score of 87.8% and an HD95 of 1.042 mm on the OASIS dataset. Experimental results substantiate the efficacy of LL -Net in capturing large receptive fields and modeling long-range spatial relationships. The code for LL -Net is available at https://github.com/BoyOfChu/LL_Net.
英文关键词	Deformable registration Light-weight network Large receptive field Anisotropic rectangular decomposition Long-range spatial relationships
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001247934900001
WOS关键词	TRANSFORMER ; MRI
WOS类目	Engineering, Biomedical
WOS研究方向	Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403032
推荐引用方式 GB/T 7714	Cao, Yuzhu,Cao, Weiwei,Wang, Ziyu,et al. A light-weight rectangular decomposition large kernel convolution network for deformable medical image registration[J],2024,95.
APA	Cao, Yuzhu.,Cao, Weiwei.,Wang, Ziyu.,Yuan, Gang.,Li, Zeyi.,...&Zheng, Jian.(2024).A light-weight rectangular decomposition large kernel convolution network for deformable medical image registration.BIOMEDICAL SIGNAL PROCESSING AND CONTROL,95.
MLA	Cao, Yuzhu,et al."A light-weight rectangular decomposition large kernel convolution network for deformable medical image registration".BIOMEDICAL SIGNAL PROCESSING AND CONTROL 95(2024).