Evaluating deformable image registration (DIR) is challenging due to the inherent trade-off between achieving high alignment accuracy and maintaining deformation regularity. However, most existing DIR works either address this trade-off inadequately or overlook it altogether. In this paper, we highlight the issues with existing practices and propose an evaluation scheme that captures the trade-off continuously to holistically evaluate DIR methods. We first introduce the alignment regularity characteristic (ARC) curves, which describe the performance of a given registration method as a spectrum under various degrees of regularity. We demonstrate that the ARC curves reveal unique insights that are not evident from existing evaluation practices, using experiments on representative deep learning DIR methods with various network architectures and transformation models. We further adopt a HyperNetwork based approach that learns to continuously interpolate across the full regularization range, accelerating the construction and improving the sample density of ARC curves. Finally, we provide general guidelines for a nuanced model evaluation and selection using our evaluation scheme for both practitioners and registration researchers.

翻译：评估可变形图像配准（DIR）具有挑战性，因为实现高配准精度与保持形变规则性之间存在固有的权衡关系。然而，现有的大多数DIR研究要么未能充分处理这种权衡，要么完全忽视了这一问题。本文指出了现有实践中的不足，并提出了一种能够连续捕捉这种权衡关系的评估方案，以全面评估DIR方法。我们首先引入了配准规则性特征（ARC）曲线，该曲线描述了给定配准方法在不同规则性程度下的性能谱。通过在具有不同网络架构和变换模型的代表性深度学习DIR方法上进行实验，我们证明了ARC曲线能够揭示现有评估实践中无法体现的独特见解。我们进一步采用基于超网络的方法，学习在整个正则化范围内进行连续插值，从而加速ARC曲线的构建并提高其样本密度。最后，我们为从业者和配准研究人员提供了使用本评估方案进行精细化模型评估与选择的一般性指导原则。