DARE: A Deformable Adaptive Regularization Estimator for Learning-Based Medical Image Registration
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914107751399424 |
|---|---|
| author | Siyal, Ahsan Raza Haltmeier, Markus Steiger, Ruth Galijasevic, Malik Gizewski, Elke Ruth Grams, Astrid Ellen |
| author_facet | Siyal, Ahsan Raza Haltmeier, Markus Steiger, Ruth Galijasevic, Malik Gizewski, Elke Ruth Grams, Astrid Ellen |
| contents | Deformable medical image registration is a fundamental task in medical image analysis. While deep learning-based methods have demonstrated superior accuracy and computational efficiency compared to traditional techniques, they often overlook the critical role of regularization in ensuring robustness and anatomical plausibility. We propose DARE (Deformable Adaptive Regularization Estimator), a novel registration framework that dynamically adjusts elastic regularization based on the gradient norm of the deformation field. Our approach integrates strain and shear energy terms, which are adaptively modulated to balance stability and flexibility. To ensure physically realistic transformations, DARE includes a folding-prevention mechanism that penalizes regions with negative deformation Jacobian. This strategy mitigates non-physical artifacts such as folding, avoids over-smoothing, and improves both registration accuracy and anatomical plausibility |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19353 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DARE: A Deformable Adaptive Regularization Estimator for Learning-Based Medical Image Registration Siyal, Ahsan Raza Haltmeier, Markus Steiger, Ruth Galijasevic, Malik Gizewski, Elke Ruth Grams, Astrid Ellen Computer Vision and Pattern Recognition Numerical Analysis Deformable medical image registration is a fundamental task in medical image analysis. While deep learning-based methods have demonstrated superior accuracy and computational efficiency compared to traditional techniques, they often overlook the critical role of regularization in ensuring robustness and anatomical plausibility. We propose DARE (Deformable Adaptive Regularization Estimator), a novel registration framework that dynamically adjusts elastic regularization based on the gradient norm of the deformation field. Our approach integrates strain and shear energy terms, which are adaptively modulated to balance stability and flexibility. To ensure physically realistic transformations, DARE includes a folding-prevention mechanism that penalizes regions with negative deformation Jacobian. This strategy mitigates non-physical artifacts such as folding, avoids over-smoothing, and improves both registration accuracy and anatomical plausibility |
| title | DARE: A Deformable Adaptive Regularization Estimator for Learning-Based Medical Image Registration |
| topic | Computer Vision and Pattern Recognition Numerical Analysis |
| url | https://arxiv.org/abs/2510.19353 |