Wavelet-Guided Multi-Scale ConvNeXt for Unsupervised Medical Image Registration
Medical image registration is essential in clinical practices such as surgical navigation and image-guided diagnosis. The Transformer architecture of TransMorph demonstrates better accuracy in non-rigid registration tasks. However, its weaker spatial locality priors necessitate large-scale training...
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2025-04-01
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| author | Xuejun Zhang Aobo Xu Ganxin Ouyang Zhengrong Xu Shaofei Shen Wenkang Chen Mingxian Liang Guiqi Zhang Jiashun Wei Xiangrong Zhou Dongbo Wu |
| author_facet | Xuejun Zhang Aobo Xu Ganxin Ouyang Zhengrong Xu Shaofei Shen Wenkang Chen Mingxian Liang Guiqi Zhang Jiashun Wei Xiangrong Zhou Dongbo Wu |
| author_sort | Xuejun Zhang |
| collection | DOAJ |
| description | Medical image registration is essential in clinical practices such as surgical navigation and image-guided diagnosis. The Transformer architecture of TransMorph demonstrates better accuracy in non-rigid registration tasks. However, its weaker spatial locality priors necessitate large-scale training datasets and a heavy number of parameters, which conflict with the limited annotated data and real-time demands of clinical workflows. Moreover, traditional downsampling and upsampling always degrade high-frequency anatomical features such as tissue boundaries or small lesions. We proposed WaveMorph, a wavelet-guided multi-scale ConvNeXt method for unsupervised medical image registration. A novel multi-scale wavelet feature fusion downsampling module is proposed by integrating the ConvNeXt architecture with Haar wavelet lossless decomposition to extract and fuse features from eight frequency sub-images using multi-scale convolution kernels. Additionally, a lightweight dynamic upsampling module is introduced in the decoder to reconstruct fine-grained anatomical structures. WaveMorph integrates the inductive bias of CNNs with the advantages of Transformers, effectively mitigating topological distortions caused by spatial information loss while supporting real-time inference. In both atlas-to-patient (IXI) and inter-patient (OASIS) registration tasks, WaveMorph demonstrates state-of-the-art performance, achieving Dice scores of 0.779 ± 0.015 and 0.824 ± 0.021, respectively, and real-time inference (0.072 s/image), validating the effectiveness of our model in medical image registration. |
| format | Article |
| id | doaj-art-09dca57a363d43d7afd6f4c6782c4f24 |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-09dca57a363d43d7afd6f4c6782c4f242025-08-20T02:17:14ZengMDPI AGBioengineering2306-53542025-04-0112440610.3390/bioengineering12040406Wavelet-Guided Multi-Scale ConvNeXt for Unsupervised Medical Image RegistrationXuejun Zhang0Aobo Xu1Ganxin Ouyang2Zhengrong Xu3Shaofei Shen4Wenkang Chen5Mingxian Liang6Guiqi Zhang7Jiashun Wei8Xiangrong Zhou9Dongbo Wu10School of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaDepartment of Electrical, Electronic and Computer Engineering, Gifu University, Gifu 501-1193, JapanSchool of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaDepartment of General Surgery, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou 545005, ChinaDepartment of General Surgery, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou 545005, ChinaDepartment of Electrical, Electronic and Computer Engineering, Gifu University, Gifu 501-1193, JapanDepartment of General Surgery, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou 545005, ChinaMedical image registration is essential in clinical practices such as surgical navigation and image-guided diagnosis. The Transformer architecture of TransMorph demonstrates better accuracy in non-rigid registration tasks. However, its weaker spatial locality priors necessitate large-scale training datasets and a heavy number of parameters, which conflict with the limited annotated data and real-time demands of clinical workflows. Moreover, traditional downsampling and upsampling always degrade high-frequency anatomical features such as tissue boundaries or small lesions. We proposed WaveMorph, a wavelet-guided multi-scale ConvNeXt method for unsupervised medical image registration. A novel multi-scale wavelet feature fusion downsampling module is proposed by integrating the ConvNeXt architecture with Haar wavelet lossless decomposition to extract and fuse features from eight frequency sub-images using multi-scale convolution kernels. Additionally, a lightweight dynamic upsampling module is introduced in the decoder to reconstruct fine-grained anatomical structures. WaveMorph integrates the inductive bias of CNNs with the advantages of Transformers, effectively mitigating topological distortions caused by spatial information loss while supporting real-time inference. In both atlas-to-patient (IXI) and inter-patient (OASIS) registration tasks, WaveMorph demonstrates state-of-the-art performance, achieving Dice scores of 0.779 ± 0.015 and 0.824 ± 0.021, respectively, and real-time inference (0.072 s/image), validating the effectiveness of our model in medical image registration.https://www.mdpi.com/2306-5354/12/4/406non-rigid medical image registrationunsupervised deep learningHaar waveletConvNeXt |
| spellingShingle | Xuejun Zhang Aobo Xu Ganxin Ouyang Zhengrong Xu Shaofei Shen Wenkang Chen Mingxian Liang Guiqi Zhang Jiashun Wei Xiangrong Zhou Dongbo Wu Wavelet-Guided Multi-Scale ConvNeXt for Unsupervised Medical Image Registration Bioengineering non-rigid medical image registration unsupervised deep learning Haar wavelet ConvNeXt |
| title | Wavelet-Guided Multi-Scale ConvNeXt for Unsupervised Medical Image Registration |
| title_full | Wavelet-Guided Multi-Scale ConvNeXt for Unsupervised Medical Image Registration |
| title_fullStr | Wavelet-Guided Multi-Scale ConvNeXt for Unsupervised Medical Image Registration |
| title_full_unstemmed | Wavelet-Guided Multi-Scale ConvNeXt for Unsupervised Medical Image Registration |
| title_short | Wavelet-Guided Multi-Scale ConvNeXt for Unsupervised Medical Image Registration |
| title_sort | wavelet guided multi scale convnext for unsupervised medical image registration |
| topic | non-rigid medical image registration unsupervised deep learning Haar wavelet ConvNeXt |
| url | https://www.mdpi.com/2306-5354/12/4/406 |
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