POLAFFINI: Efficient feature-based polyaffine initialization for improved non-linear image registration
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929414875381760 |
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| author | Legouhy, Antoine Callaghan, Ross Azadbakht, Hojjat Zhang, Hui |
| author_facet | Legouhy, Antoine Callaghan, Ross Azadbakht, Hojjat Zhang, Hui |
| contents | This paper presents an efficient feature-based approach to initialize non-linear image registration. Today, nonlinear image registration is dominated by methods relying on intensity-based similarity measures. A good estimate of the initial transformation is essential, both for traditional iterative algorithms and for recent one-shot deep learning (DL)-based alternatives. The established approach to estimate this starting point is to perform affine registration, but this may be insufficient due to its parsimonious, global, and non-bending nature. We propose an improved initialization method that takes advantage of recent advances in DL-based segmentation techniques able to instantly estimate fine-grained regional delineations with state-of-the-art accuracies. Those segmentations are used to produce local, anatomically grounded, feature-based affine matchings using iteration-free closed-form expressions. Estimated local affine transformations are then fused, with the log-Euclidean polyaffine framework, into an overall dense diffeomorphic transformation. We show that, compared to its affine counterpart, the proposed initialization leads to significantly better alignment for both traditional and DL-based non-linear registration algorithms. The proposed approach is also more robust and significantly faster than commonly used affine registration algorithms such as FSL FLIRT. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_03922 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | POLAFFINI: Efficient feature-based polyaffine initialization for improved non-linear image registration Legouhy, Antoine Callaghan, Ross Azadbakht, Hojjat Zhang, Hui Computer Vision and Pattern Recognition This paper presents an efficient feature-based approach to initialize non-linear image registration. Today, nonlinear image registration is dominated by methods relying on intensity-based similarity measures. A good estimate of the initial transformation is essential, both for traditional iterative algorithms and for recent one-shot deep learning (DL)-based alternatives. The established approach to estimate this starting point is to perform affine registration, but this may be insufficient due to its parsimonious, global, and non-bending nature. We propose an improved initialization method that takes advantage of recent advances in DL-based segmentation techniques able to instantly estimate fine-grained regional delineations with state-of-the-art accuracies. Those segmentations are used to produce local, anatomically grounded, feature-based affine matchings using iteration-free closed-form expressions. Estimated local affine transformations are then fused, with the log-Euclidean polyaffine framework, into an overall dense diffeomorphic transformation. We show that, compared to its affine counterpart, the proposed initialization leads to significantly better alignment for both traditional and DL-based non-linear registration algorithms. The proposed approach is also more robust and significantly faster than commonly used affine registration algorithms such as FSL FLIRT. |
| title | POLAFFINI: Efficient feature-based polyaffine initialization for improved non-linear image registration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.03922 |