Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1002/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
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ISSN | 0094-2405 |
EISSN | 2473-4209 |
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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