干旱区生态环境知识资源中心(Arid): MFCTrans: Multi-scale Feature Connection Transformer for Deformable Medical Image Registration

Arid

DOI	10.1007/s12559-023-10239-z
	MFCTrans: Multi-scale Feature Connection Transformer for Deformable Medical Image Registration
	Wang, Longji; Yan, Zhiyue; Cao, Wenming; Ji, Jianhua
通讯作者	Cao, WM
来源期刊	COGNITIVE COMPUTATION
ISSN	1866-9956
EISSN	1866-9964
出版年	2024
卷号	16 期号:3 页码:1125-1140
英文摘要	Deformable Medical Image Registration (DMIR) aims to establish precise anatomical alignment of multiple medical images. However, the existing U-shape networks encounter difficulties in efficiently transferring multi-scale feature information from the encoder to the decoder. To address this issue, we propose a novel backbone network called MFCTrans, which constructs effective feature connection in DMIR. Drawing inspiration from the attention mechanism observed in the human cognitive system, our proposed method employs a Feature Fusion and Assignment Transformer (FFAT) module and a Spatial Cross Attention Fusion (SCAF) module. The former facilitates the fusion of multi-channel features, while the latter guides the integration of multi-scale information. A Multiple Residual (MR) branch is also deployed between the encoder and FFAT to improve the network's generalization. We conduct extensive qualitative and quantitative evaluations on the OASIS and LPBA40 datasets. The proposed method achieves higher Dice scores than Transmorph by 1.3% and 2.0% on the respective datasets while maintaining a comparable voxel folding percentage. Ablation studies analyze the impacts and efficiency of each component in the proposed method. In summary, our proposed network offers a promising framework for achieving high-quality medical image registration and holds significant potential for applications in computer vision and cognitive computation.
英文关键词	Deformable medical image registration Multi-scale feature fusion Channel cross attention Spatial cross attention
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001147724100002
WOS关键词	U-NET ARCHITECTURE ; LEARNING FRAMEWORK ; REGULARIZATION
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS研究方向	Computer Science ; Neurosciences & Neurology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403220
推荐引用方式 GB/T 7714	Wang, Longji,Yan, Zhiyue,Cao, Wenming,et al. MFCTrans: Multi-scale Feature Connection Transformer for Deformable Medical Image Registration[J],2024,16(3):1125-1140.
APA	Wang, Longji,Yan, Zhiyue,Cao, Wenming,&Ji, Jianhua.(2024).MFCTrans: Multi-scale Feature Connection Transformer for Deformable Medical Image Registration.COGNITIVE COMPUTATION,16(3),1125-1140.
MLA	Wang, Longji,et al."MFCTrans: Multi-scale Feature Connection Transformer for Deformable Medical Image Registration".COGNITIVE COMPUTATION 16.3(2024):1125-1140.