Correlation Ratio for Unsupervised Learning of Multi-modal Deformable Registration
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910912831553536 |
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| author | Chen, Xiaojian Liu, Yihao Wei, Shuwen Carass, Aaron Du, Yong Chen, Junyu |
| author_facet | Chen, Xiaojian Liu, Yihao Wei, Shuwen Carass, Aaron Du, Yong Chen, Junyu |
| contents | In recent years, unsupervised learning for deformable image registration has been a major research focus. This approach involves training a registration network using pairs of moving and fixed images, along with a loss function that combines an image similarity measure and deformation regularization. For multi-modal image registration tasks, the correlation ratio has been a widely-used image similarity measure historically, yet it has been underexplored in current deep learning methods. Here, we propose a differentiable correlation ratio to use as a loss function for learning-based multi-modal deformable image registration. This approach extends the traditionally non-differentiable implementation of the correlation ratio by using the Parzen windowing approximation, enabling backpropagation with deep neural networks. We validated the proposed correlation ratio on a multi-modal neuroimaging dataset. In addition, we established a Bayesian training framework to study how the trade-off between the deformation regularizer and similarity measures, including mutual information and our proposed correlation ratio, affects the registration performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12265 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Correlation Ratio for Unsupervised Learning of Multi-modal Deformable Registration Chen, Xiaojian Liu, Yihao Wei, Shuwen Carass, Aaron Du, Yong Chen, Junyu Image and Video Processing In recent years, unsupervised learning for deformable image registration has been a major research focus. This approach involves training a registration network using pairs of moving and fixed images, along with a loss function that combines an image similarity measure and deformation regularization. For multi-modal image registration tasks, the correlation ratio has been a widely-used image similarity measure historically, yet it has been underexplored in current deep learning methods. Here, we propose a differentiable correlation ratio to use as a loss function for learning-based multi-modal deformable image registration. This approach extends the traditionally non-differentiable implementation of the correlation ratio by using the Parzen windowing approximation, enabling backpropagation with deep neural networks. We validated the proposed correlation ratio on a multi-modal neuroimaging dataset. In addition, we established a Bayesian training framework to study how the trade-off between the deformation regularizer and similarity measures, including mutual information and our proposed correlation ratio, affects the registration performance. |
| title | Correlation Ratio for Unsupervised Learning of Multi-modal Deformable Registration |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2504.12265 |