动态调节形变正则化，提升医学图像配准的准确性与解剖合理性。
DARE: A Deformable Adaptive Regularization Estimator for Learning-Based Medical Image Registration
- 根据形变场梯度自适应调整弹性正则化强度。
- 引入剪切与应变能量项，平衡稳定与灵活性。
- 通过雅可比行列式惩罚防止形变折叠，适合临床配准场景。
可变形医学图像配准是医学图像分析的基础任务。尽管基于深度学习的方法在精度和计算效率上优于传统技术，但常忽视正则化对鲁棒性和解剖合理性的重要性。我们提出 DARE（可变形自适应正则化估计器），一种新型配准框架，根据形变场的梯度范数动态调整弹性正则化。该方法融合剪切能与应变能项，实现自适应调节以平衡稳定性与灵活性。为确保物理合理性，DARE 引入折叠预防机制，对负雅可比行列式区域施加惩罚。该策略有效缓解非物理伪影，避免过度平滑，同时提升配准精度与解剖合理性。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。