Pulmonary CT Registration Network Based on Deformable Cross Attention.
Current Transformer structure utilizes the self-attention mechanism to model global contextual relevance within image, which makes an impact on medical image registration. However, the use of Transformer in handling large deformation lung CT registration is relatively straightforwardly. These models...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 1963 - 1976 |
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| Main Authors: | , , , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Aug2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187278963&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187278963 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2025 vid: 38 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187278963 187278963 187278963 10.1007/s10278-024-01324-2 187278963 ppf: 1963 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Pulmonary CT Registration Network Based on Deformable Cross Attention. aug: au: Ren, Meirong Xue, Peng Ji, Huizhong Zhang, Zhili Dong, Enqing affil: https://ror.org/0207yh398 Shool of Mechanical, Electrical & Information Engineering, Shandong University, 264,209, Weihai, China sug: subj: Lung Radiography Tomography, X-Ray Computed Image Processing, Computer Assisted Human Deep Learning Algorithms Attention Neural Networks (Computer) Reproducibility of Results Sensitivity and Specificity Qualitative Studies Funding Source ab: Current Transformer structure utilizes the self-attention mechanism to model global contextual relevance within image, which makes an impact on medical image registration. However, the use of Transformer in handling large deformation lung CT registration is relatively straightforwardly. These models only focus on single image feature representation neglecting to employ attention mechanism to capture the across image correspondence. This hinders further improvement in registration performance. To address the above limitations, we propose a novel registration method in a cascaded manner, Cascaded Swin Deformable Cross Attention Transformer based U-shape structure (SD-CATU), to address the challenge of large deformations in lung CT registration. In SD-CATU, we introduce a Cross Attention-based Transformer (CAT) block that incorporates the Shifted Regions Multihead Cross-attention (SR-MCA) mechanism to flexibly exchange feature information and thus reduce the computational complexity. Besides, a consistency constraint in the loss function is used to ensure the preservation of topology and inverse consistency of the transformations. Experiments with public lung datasets demonstrate that the Cascaded SD-CATU outperforms current state-of-the-art registration methods (Dice Similarity Coefficient of 93.19% and Target registration error of 0.98 mm). The results further highlight the potential for obtaining excellent registration accuracy while assuring desirable smoothness and consistency in the deformed images. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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