With recent advances in computing hardware and surges of deep-learning architectures, learning-based deep image registration methods have surpassed their traditional counterparts, in terms of metric performance and inference time. However, these methods focus on improving performance measurements such as Dice, resulting in less attention given to model behaviors that are equally desirable for registrations, especially for medical imaging. This paper investigates these behaviors for popular learning-based deep registrations under a sanity-checking microscope. We find that most existing registrations suffer from low inverse consistency and nondiscrimination of identical pairs due to overly optimized image similarities. To rectify these behaviors, we propose a novel regularization-based sanity-enforcer method that imposes two sanity checks on the deep model to reduce its inverse consistency errors and increase its discriminative power simultaneously. Moreover, we derive a set of theoretical guarantees for our sanity-checked image registration method, with experimental results supporting our theoretical findings and their effectiveness in increasing the sanity of models without sacrificing any performance. Our code and models are available at https://github.com/tuffr5/Saner-deep-registration.

翻译：随着计算硬件的进步和深度学习架构的兴起，基于学习的深度图像配准方法在指标性能和推理时间方面已超越传统方法。然而，这些方法过度聚焦于Dice等性能指标的优化，导致对配准中同样重要的模型行为（特别是医学影像领域）关注不足。本文通过理性检查的视角，系统研究了主流学习型深度配准方法的行为特征。我们发现：由于过度优化图像相似度，现有大多数配准方法存在逆一致性低下与同源配对鉴别失效的问题。为纠正这些行为，我们提出一种基于正则化的理性增强方法，通过对深度模型施加两项理性检查约束，同时降低其逆一致性误差并提升判别能力。此外，我们为这种理性检查图像配准方法推导了理论保证，实验结果支持了理论发现及其在提升模型理性程度方面的有效性，且未牺牲任何性能指标。相关代码与模型已开源发布于https://github.com/tuffr5/Saner-deep-registration。