Hybrid transformer and convolution iteratively optimized pyramid network for brain large deformation image registration
Abstract In recent years, the pyramid-based encoder-decoder network architecture has become a popular solution to the problem of large deformation image registration due to its excellent multi-scale deformation field prediction ability. However, there are two main limitations in existing research: o...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-00403-w |
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| author | Xinxin Cui Yuee Zhou Caihong Wei Guodong Suo Fengqing Jin Jianlan Yang |
| author_facet | Xinxin Cui Yuee Zhou Caihong Wei Guodong Suo Fengqing Jin Jianlan Yang |
| author_sort | Xinxin Cui |
| collection | DOAJ |
| description | Abstract In recent years, the pyramid-based encoder-decoder network architecture has become a popular solution to the problem of large deformation image registration due to its excellent multi-scale deformation field prediction ability. However, there are two main limitations in existing research: one is that it over-focuses on the fusion of multi-layer deformation sub-fields on the decoding path, while ignoring the impact of feature encoders on network performance; the other is the lack of specialized design for the characteristics of feature maps at different scales. To this end, we propose an innovative hybrid Transformer and convolution iteratively optimized pyramid network for large deformation brain image registration. Specifically, four encoder variants are designed to study the impact of different structures on the performance of the pyramid registration network. Secondly, the Swin-Transformer module is combined with the convolution iterative strategy, and each layer of the decoder is carefully designed according to the semantic information characteristics of different decoding layers. Extensive experimental results on three public brain magnetic resonance imaging datasets show that our method has the highest registration accuracy compared with 9 cutting-edge registration methods, which fully verifies the effectiveness and application potential of our model design. |
| format | Article |
| id | doaj-art-b73cb466f9ee4b9c8fc42d57ce21c676 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b73cb466f9ee4b9c8fc42d57ce21c6762025-08-20T03:53:12ZengNature PortfolioScientific Reports2045-23222025-05-0115111810.1038/s41598-025-00403-wHybrid transformer and convolution iteratively optimized pyramid network for brain large deformation image registrationXinxin Cui0Yuee Zhou1Caihong Wei2Guodong Suo3Fengqing Jin4Jianlan Yang5School of Medical Information Engineering, Gansu University of Traditional Chinese MedicineSchool of Medical Information Engineering, Gansu University of Traditional Chinese MedicineQuanzhou Orthopedic Traumatological Hospital of Fujian University of Traditional Chinese MedicineSchool of Medical Information Engineering, Gansu University of Traditional Chinese MedicineSchool of Medical Information Engineering, Gansu University of Traditional Chinese MedicineSchool of Medical Information Engineering, Gansu University of Traditional Chinese MedicineAbstract In recent years, the pyramid-based encoder-decoder network architecture has become a popular solution to the problem of large deformation image registration due to its excellent multi-scale deformation field prediction ability. However, there are two main limitations in existing research: one is that it over-focuses on the fusion of multi-layer deformation sub-fields on the decoding path, while ignoring the impact of feature encoders on network performance; the other is the lack of specialized design for the characteristics of feature maps at different scales. To this end, we propose an innovative hybrid Transformer and convolution iteratively optimized pyramid network for large deformation brain image registration. Specifically, four encoder variants are designed to study the impact of different structures on the performance of the pyramid registration network. Secondly, the Swin-Transformer module is combined with the convolution iterative strategy, and each layer of the decoder is carefully designed according to the semantic information characteristics of different decoding layers. Extensive experimental results on three public brain magnetic resonance imaging datasets show that our method has the highest registration accuracy compared with 9 cutting-edge registration methods, which fully verifies the effectiveness and application potential of our model design.https://doi.org/10.1038/s41598-025-00403-wEnhanced pyramid encoderConvolution iterative optimizationTransformerBrain MRI |
| spellingShingle | Xinxin Cui Yuee Zhou Caihong Wei Guodong Suo Fengqing Jin Jianlan Yang Hybrid transformer and convolution iteratively optimized pyramid network for brain large deformation image registration Scientific Reports Enhanced pyramid encoder Convolution iterative optimization Transformer Brain MRI |
| title | Hybrid transformer and convolution iteratively optimized pyramid network for brain large deformation image registration |
| title_full | Hybrid transformer and convolution iteratively optimized pyramid network for brain large deformation image registration |
| title_fullStr | Hybrid transformer and convolution iteratively optimized pyramid network for brain large deformation image registration |
| title_full_unstemmed | Hybrid transformer and convolution iteratively optimized pyramid network for brain large deformation image registration |
| title_short | Hybrid transformer and convolution iteratively optimized pyramid network for brain large deformation image registration |
| title_sort | hybrid transformer and convolution iteratively optimized pyramid network for brain large deformation image registration |
| topic | Enhanced pyramid encoder Convolution iterative optimization Transformer Brain MRI |
| url | https://doi.org/10.1038/s41598-025-00403-w |
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