Chinese Journal of Magnetic Resonance >
Deformable Registration Network Based on Multi-scale Dilated Residual and Dual Attention
Received date: 2025-11-28
Online published: 2026-08-11
Deformable registration plays a significant role in multiple medical image analysis tasks. However, the fixed receptive field of convolutional neural networks makes it difficult to fully capture the spatial context information of the brain. While the Transformer architecture can effectively capture global information, it suffers from high computational cost. To address this dilemma, we propose a deformable registration network based on multi-scale dilated residual convolution and dual attention. This network employs a multi-scale dilated residual convolution to capture local details and extensive context information simultaneously. A dual attention module is integrated to enhance feature representation capability. The decoder section introduces dynamic upsampling to precisely reconstruct high-frequency details, thereby ensuring the topological integrity of the deformation field. Experiments were conducted on two public brain datasets. The results demonstrate that the proposed model achieves high registration performance across multiple metrics, including Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and percentage of non-positive Jacobian determinant, indicating superior registration precision and smoother deformation fields.
YANG Jingjing , WANG Yuanjun . Deformable Registration Network Based on Multi-scale Dilated Residual and Dual Attention[J]. Chinese Journal of Magnetic Resonance, 2026 , 43(3) : 291 -306 . DOI: 10.11938/cjmr20253190
| [1] | ZHU H Y, LI M, JI Y L, et al. A body examination study of brain MRI registration in adolescents using fusion generative adversarial network[J]. Chinese J Magn Reson Imaging, 2023, 14(2): 116-124. |
| 朱海艳, 李盟, 季跃龙, 等. 融合生成对抗网络的青少年脑MRI配准体测研究[J]. 磁共振成像, 2023, 14(2): 116-124. | |
| [2] | ZHOU Q, WANG Y J. Groupwise registration for magnetic resonance image based on variational inference[J]. Chinese J Magn Reson, 2022, 39(3): 291-302. |
| 周勤, 王远军. 基于变分推断的磁共振图像群组配准[J]. 波谱学杂志, 2022, 39(3): 291-302. | |
| [3] | BAN Y, WANG Y, LIU S, et al. 2D/3D multimode medical image alignment based on spatial histograms[J]. Appl Sci, 2022, 12(16): 8261. |
| [4] | BOSMA L S, ZACHIU C, SENNEVILLE B D D, et al. Intensity-based quality assurance criteria for deformable image registration in image-guided radiotherapy[J]. Med Phys, 2023, 50(9): 5715-5722. |
| [5] | XUE P, DONG E, JI H. Lung 4D CT image registration based on high-order markov random field[J]. IEEE Trans Med Imaging, 2019, 39(4): 910-921. |
| [6] | MANERA A L, DADAR M, FONOV V, et al. CerebrA, registration and manual label correction of Mindboggle-101 atlas for MNI-ICBM152 template[J]. Sci Data, 2020, 7(1): 237. |
| [7] | BALAKRISHNAN G, ZHAO A, SABUNCU M R, et al. Voxelmorph: a learning framework for deformable medical image registration[J]. IEEE Trans Med Imaging, 2019, 38(8): 1788-1800. |
| [8] | CHEN X, WANG X, ZHANG K, et al. Recent advances and clinical applications of deep learning in medical image analysis[J]. Med Image Anal, 2022, 79: 102444. |
| [9] | JADERBERG M, SIMONYAN K, ZISSERMAN A. Spatial transformer networks[J]. Adv Neural Inf Process Syst, 2015, 28: 2017-2025. |
| [10] | KIM B, KIM D H, PARK S H, et al. CycleMorph: cycle consistent unsupervised deformable image registration[J]. Med Image Anal, 2021, 71: 102036. |
| [11] | MOK T C W, CHUNG A. Fast symmetric diffeomorphic image registration with convolutional neural networks[C]// Proceedings of the IEEE/CVF International Conference on Computer Vision. Seattle, WA, USA: IEEE, 2020: 4644-4653. |
| [12] | VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// Advances in Neural Information Processing Systems 30. Long Beach, CA, USA: Curran Associates, 2017: 5998-6008. |
| [13] | DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words:Transformers for image recognition at scale[PP/OL]. arXiv (2021-06-03)[2025-11-25]. https://arxiv.org/abs/2010.11929. |
| [14] | CHEN J, HE Y, FREY E C, et al. Vit-V-Net: Vision transformer for unsupervised volumetric medical image registration[PP/OL]. arXiv 2021-04-13)[2025-11-25]. https://arxiv.org/abs/2104.06468. |
| [15] | CHEN J, FREY E C, HE Y, et al. Transmorph: Transformer for unsupervised medical image registration[J]. Med Image Anal, 2022, 82: 102615. |
| [16] | LIU Z, LIN Y, CAO Y, et al. Swin transformer: Hierarchical vision transformer using shifted windows[C]// Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal, QC, Canada: IEEE, 2021: 10012-10022. |
| [17] | SHI J, HE Y, KONG Y, et al. Xmorpher: Full transformer for deformable medical image registration via cross attention[C]// International Conference on Medical Image Computing and Computer-Assisted Intervention. Singapore: Springer, 2022: 217-226. |
| [18] | ZHENG J Q, WANG Z, HUANG B, et al. Recursive deformable image registration network with mutual attention[C]// Annual Conference on Medical Image Understanding and Analysis. Cambridge, UK: Springer, 2022: 75-86. |
| [19] | WANG H, NI D, WANG Y. Modet: Learning deformable image registration via motion decomposition transformer[C]// International Conference on Medical Image Computing and Computer-Assisted Intervention. Vancouver, BC, Canada: Springer, 2023: 740-749. |
| [20] | CHEN Z, ZHENG Y, GEE J C. Transmatch: A transformer-based multilevel dual-stream feature matching network for unsupervised deformable image registration[J]. IEEE Trans Med Imaging, 2023, 43(1): 15-27. |
| [21] | LIU Z, MAO H, WU C Y, et al. A convnet for the 2020s[C]// Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. New Orleans, LA, USA: IEEE, 2022: 11976-11986. |
| [22] | CHOLLET F. Xception: Deep learning with depthwise separable convolutions[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. Honolulu, HI, USA: IEEE, 2017: 1251-1258. |
| [23] | LIU W, LU H, FU H, et al. Learning to upsample by learning to sample[C]// Proceedings of the IEEE/CVF international conference on computer vision. Paris, France: IEEE, 2023: 6027-6037. |
| [24] | WANG J, CHEN K, XU R, et al. Carafe: Content-aware reassembly of features[C]// Proceedings of the IEEE/CVF international conference on computer vision. Seoul, Korea: IEEE, 2019: 3007-3016. |
| [25] | LU H, LIU W, FU H, et al. FADE: Fusing the assets of decoder and encoder for task-agnostic upsampling[C]// European conference on computer vision. Tel Aviv, Israel: Springer, 2022: 231-247. |
| [26] | RAO Y R, PRATHAPANI N, NAGABHOOSHANAM E. Application of normalized cross correlation to image registration[J]. Int J Res Eng Technol, 2014, 3(5): 12-16. |
| [27] | FISCHL B. FreeSurfer[J]. NeuroImage, 2012, 62(2): 774-781. |
| [28] | SHATTUCK D W, MIRZA M, ADISETIYO V, et al. Construction of a 3D probabilistic atlas of human cortical structures[J]. NeuroImage, 2008, 39(3): 1064-1080. |
| [29] | AVANTS B B, EPSTEIN C L, GROSSMAN M, et al. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain[J]. Med Image Anal, 2008, 12(1): 26-41. |
| [30] | MODAT M, RIDGWAY G R, TAYLOR Z A, et al. Fast free-form deformation using graphics processing units[J]. Comput Meth Prog Bio, 2010, 98(3): 278-284. |
| [31] | DICE L R. Measures of the amount of ecologic association between species[J]. Ecology, 1945, 26(3): 297-302. |
| [32] | TAHA A A, HANBURY A. An efficient algorithm for calculating the exact Hausdorff distance[J]. IEEE Trans Pattern Anal Mach Intell, 2015, 37(11): 2153-2163. |
| [33] | CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]// Proceedings of the European conference on computer vision. Munich, Germany: Springer, 2018: 801-818. |
| [34] | WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional block attention module[C]// Proceedings of the European conference on computer vision. Munich, Germany: Springer, 2018: 3-19. |
| [35] | HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. Salt Lake City, UT, USA: IEEE, 2018: 7132-7141. |
/
| 〈 |
|
〉 |