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DARE: A Deformable Adaptive Regularization Estimator for learning-based medical image registration
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DOI：10.1016/j.artmed.2026.103440.png)
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Abstract
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• We introduce DARE, a novel Deformable Adaptive Regularization Estimator for learning-based medical image registration. It is an unsupervised method designed to enhance both anatomical plausibility and registration accuracy. • Our approach incorporates adaptively modulated strain and shear energy terms to dynamically balance stability and flexibility, addressing the limitations of traditional uniform regularization in learning-based methods. • The proposed framework is highly adaptive and context-aware, making it particularly effective for clinical applications that require precision and structural consistency. It demonstrates strong potential for improving the reliability and effectiveness of medical image registration.
Keywords:
Medical image registration
Deep learning
Adaptive regularization
Physics informed network
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