Arid
DOI10.1002/mp.15420
TransDIR: Deformable imaging registration network based on transformer to improve the feature extraction ability
Yang, Tiejun; Bai, Xinhao; Cui, Xiaojuan; Gong, Yuehong; Li, Lei
通讯作者Bai, XH
来源期刊MEDICAL PHYSICS
ISSN0094-2405
EISSN2473-4209
出版年2022
卷号49期号:2页码:952-965
英文摘要Purpose Imaging registration has a significant contribution to guide and support physicians in the process of decision-making for diagnosis, prognosis, and treatment. However, existing registration methods based on the convolutional neural network cannot extract global features effectively, which significantly influences registration performance. Moreover, the smoothness of the displacement vector field (DVF) fails to be ensured due to the miss folding penalty. Methods In order to capture abundant global information as well as local information, we have proposed a novel 3D deformable image registration network based on Transformer (TransDIR). In the encoding phase, the transformer with the atrous reduction attention block is designed to capture the long-distance dependencies that are crucial for extracting global information. A zero-padding position encoder is embedded into the transformer to capture the local information. In the decoding phase, an up-sampling module based on an attention mechanism is designed to increase the significance of ROIs. Because of adding folding penalty term into loss function, the smoothness of DVF is improved. Results Finally, we carried out experiments on OASIS, LPBA40, MGH10, and MM-WHS open datasets to validate the effectiveness of TransDIR. Compared with LapIRN, the DSC score is improved by 1.1% and 0.9% on OASIS and LPBA40, separately. In addition, compared with VoxelMorph, the DSC score is improved by 2.8% on the basis of the folding index decreased by hundreds of times on MM-WHS. Conclusions The results show that the TransDIR achieves robust registration and promising generalizability compared with LapIRN and VoxelMorph.
英文关键词global feature extraction registration transformer zero padding
类型Article
语种英语
收录类别SCI-E
WOS记录号WOS:000744443300001
WOS关键词WHOLE HEART SEGMENTATION ; LEARNING FRAMEWORK
WOS类目Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向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/2XILL650/376543
推荐引用方式
GB/T 7714
Yang, Tiejun,Bai, Xinhao,Cui, Xiaojuan,et al. TransDIR: Deformable imaging registration network based on transformer to improve the feature extraction ability[J],2022,49(2):952-965.
APA Yang, Tiejun,Bai, Xinhao,Cui, Xiaojuan,Gong, Yuehong,&Li, Lei.(2022).TransDIR: Deformable imaging registration network based on transformer to improve the feature extraction ability.MEDICAL PHYSICS,49(2),952-965.
MLA Yang, Tiejun,et al."TransDIR: Deformable imaging registration network based on transformer to improve the feature extraction ability".MEDICAL PHYSICS 49.2(2022):952-965.
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