Fast Diffeomorphic Image Registration using Patch based Fully Convolutional Networks
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914783443288064 |
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| author | Wu, Jiong Zhou, Shuang Lin, Li Wang, Xin Tan, Wenxue |
| author_facet | Wu, Jiong Zhou, Shuang Lin, Li Wang, Xin Tan, Wenxue |
| contents | Diffeomorphic image registration is a fundamental step in medical image analysis, owing to its capability to ensure the invertibility of transformations and preservation of topology. Currently, unsupervised learning-based registration techniques primarily extract features at the image level, potentially limiting their efficacy. This paper proposes a novel unsupervised learning-based fully convolutional network (FCN) framework for fast diffeomorphic image registration, emphasizing feature acquisition at the image patch level. Furthermore, a novel differential operator is introduced and integrated into the FCN architecture for parameter learning. Experiments are conducted on three distinct T1-weighted magnetic resonance imaging (T1w MRI) datasets. Comparative analyses with three state-of-the-art diffeomorphic image registration approaches including a typical conventional registration algorithm and two representative unsupervised learning-based methods, reveal that the proposed method exhibits superior performance in both registration accuracy and topology preservation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_04244 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast Diffeomorphic Image Registration using Patch based Fully Convolutional Networks Wu, Jiong Zhou, Shuang Lin, Li Wang, Xin Tan, Wenxue Computer Vision and Pattern Recognition Diffeomorphic image registration is a fundamental step in medical image analysis, owing to its capability to ensure the invertibility of transformations and preservation of topology. Currently, unsupervised learning-based registration techniques primarily extract features at the image level, potentially limiting their efficacy. This paper proposes a novel unsupervised learning-based fully convolutional network (FCN) framework for fast diffeomorphic image registration, emphasizing feature acquisition at the image patch level. Furthermore, a novel differential operator is introduced and integrated into the FCN architecture for parameter learning. Experiments are conducted on three distinct T1-weighted magnetic resonance imaging (T1w MRI) datasets. Comparative analyses with three state-of-the-art diffeomorphic image registration approaches including a typical conventional registration algorithm and two representative unsupervised learning-based methods, reveal that the proposed method exhibits superior performance in both registration accuracy and topology preservation. |
| title | Fast Diffeomorphic Image Registration using Patch based Fully Convolutional Networks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.04244 |