DARE: A Deformable Adaptive Regularization Estimator for learning-based medical image registration.

Abstract

Deformable medical image registration is a fundamental task in medical image analysis. While deep learning-based methods have demonstrated superior accuracy and efficiency, 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. The method integrates strain and shear energy terms, adaptively modulated to balance deformation stability and flexibility, and includes a folding-prevention mechanism that penalizes regions with negative deformation Jacobian to reduce folding artifacts and encourage topologically plausible mappings. Extensive experiments on the IXI, OASIS, and MUI-P datasets demonstrate that DARE consistently improves registration accuracy while preserving anatomical plausibility. Compared with classical regularizers and state-of-the-art learning-based methods, DARE achieves higher Dice scores and reduces folding artifacts, while maintaining low strain energy and realistic volume changes in key brain structures. These results confirm that the proposed adaptive regularization mechanism delivers robust, accurate, and physiologically consistent deformation fields across clinical scenarios related to brain registration.