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：随着计算硬件的近期进展和深度学习架构的兴起，基于学习的深度图像配准方法在性能指标和推理时间上已超越传统方法。然而，这些方法专注于提升Dice等性能指标，导致对配准中同样重要的模型行为（尤其是在医学成像领域）关注不足。本文在"理智检查"显微镜下研究了流行的基于学习的深度配准方法的相关行为。我们发现，由于过度优化图像相似性，大多数现有配准存在逆一致性低和无法区分相同图像对的问题。为纠正这些行为，我们提出一种基于正则化的新颖理智增强方法，该方法对深度模型施加两项理智检查，以同时降低其逆一致性误差并提升其判别能力。此外，我们为这种经过理智检查的图像配准方法推导了一系列理论保证，实验结果支持我们的理论发现及其在不牺牲任何性能的情况下提升模型理智性的有效性。我们的代码和模型可在 https://github.com/tuffr5/Saner-deep-registration 获取。