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.

本研究论文调查了流行的基于深度学习的图像配准方法的模型行为，并提出了一种基于正规化的新颖的合理性验证方法来减少逆一致性错误并同时增加鉴别能力。通过实验结果支持理论发现，证明了该方法在提高模型合理性方面的有效性。

迈向更合理的深度图像配准

Towards Saner Deep Image Registration

Inverse consistency is a desirable property for image registration. We
propose a simple technique to make a neural registration network inverse
consistent by construction, as a consequence of its structure, as long as it
parameterizes its output transform by a Lie group. We extend this technique to
multi-step neural registration by composing many such networks in a way that
preserves inverse consistency. This multi-step approach also allows for
inverse-consistent coarse to fine registration. We evaluate our technique on
synthetic 2-D data and four 3-D medical image registration tasks and obtain
excellent registration accuracy while assuring inverse consistency.

提出了一种将神经网络逆一致化的简单技术，通过构建其结构，只要它将输出变换参数化为李群。将该技术扩展到多步神经注册，通过以保持逆一致性的方式组成许多这样的网络。我们在合成的 2D 数据和四个 3D 医学图像配准任务上评估了我们的技术，在确保逆一致性的同时获得了出色的配准精度。