A CNN‐transformer‐based unsupervised aware hierarchical network for medical image registration
Abstract Medical image registration is a fundamental and important technique in the field of medical image analysis. This study proposes a novel unsupervised end‐to‐end registration network, aiming to enable the model to actively acquire image features in the field of medical imaging with limited sa...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | Electronics Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ell2.70124 |
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| _version_ | 1846108042474553344 |
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| author | Bo Fang Lisheng Wang |
| author_facet | Bo Fang Lisheng Wang |
| author_sort | Bo Fang |
| collection | DOAJ |
| description | Abstract Medical image registration is a fundamental and important technique in the field of medical image analysis. This study proposes a novel unsupervised end‐to‐end registration network, aiming to enable the model to actively acquire image features in the field of medical imaging with limited samples, which efficiently integrates multi‐scale features to achieve higher accuracy in registration. By utilizing region‐to‐region routing, this model actively preserves the most relevant features of the images, thereby improving training and learning efficiency. The model is evaluated by several publicly available datasets. The new network proposed in this study achieved the best registration accuracy among various advanced traditional and learning‐based methods. |
| format | Article |
| id | doaj-art-fdb86f64efcf4a368dab366e7a4cef32 |
| institution | Kabale University |
| issn | 0013-5194 1350-911X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Electronics Letters |
| spelling | doaj-art-fdb86f64efcf4a368dab366e7a4cef322024-12-26T07:11:10ZengWileyElectronics Letters0013-51941350-911X2024-12-016024n/an/a10.1049/ell2.70124A CNN‐transformer‐based unsupervised aware hierarchical network for medical image registrationBo Fang0Lisheng Wang1School of Medicine GuangXi University Nanning ChinaSchool of Medicine GuangXi University Nanning ChinaAbstract Medical image registration is a fundamental and important technique in the field of medical image analysis. This study proposes a novel unsupervised end‐to‐end registration network, aiming to enable the model to actively acquire image features in the field of medical imaging with limited samples, which efficiently integrates multi‐scale features to achieve higher accuracy in registration. By utilizing region‐to‐region routing, this model actively preserves the most relevant features of the images, thereby improving training and learning efficiency. The model is evaluated by several publicly available datasets. The new network proposed in this study achieved the best registration accuracy among various advanced traditional and learning‐based methods.https://doi.org/10.1049/ell2.70124artificial intelligenceimage fusionimage registrationlearning (artificial intelligence) |
| spellingShingle | Bo Fang Lisheng Wang A CNN‐transformer‐based unsupervised aware hierarchical network for medical image registration Electronics Letters artificial intelligence image fusion image registration learning (artificial intelligence) |
| title | A CNN‐transformer‐based unsupervised aware hierarchical network for medical image registration |
| title_full | A CNN‐transformer‐based unsupervised aware hierarchical network for medical image registration |
| title_fullStr | A CNN‐transformer‐based unsupervised aware hierarchical network for medical image registration |
| title_full_unstemmed | A CNN‐transformer‐based unsupervised aware hierarchical network for medical image registration |
| title_short | A CNN‐transformer‐based unsupervised aware hierarchical network for medical image registration |
| title_sort | cnn transformer based unsupervised aware hierarchical network for medical image registration |
| topic | artificial intelligence image fusion image registration learning (artificial intelligence) |
| url | https://doi.org/10.1049/ell2.70124 |
| work_keys_str_mv | AT bofang acnntransformerbasedunsupervisedawarehierarchicalnetworkformedicalimageregistration AT lishengwang acnntransformerbasedunsupervisedawarehierarchicalnetworkformedicalimageregistration AT bofang cnntransformerbasedunsupervisedawarehierarchicalnetworkformedicalimageregistration AT lishengwang cnntransformerbasedunsupervisedawarehierarchicalnetworkformedicalimageregistration |