Deep learning has emerged as a strong alternative for classical iterative methods for deformable medical image registration, where the goal is to find a mapping between the coordinate systems of two images. Popular classical image registration methods enforce the useful inductive biases of symmetricity, inverse consistency, and topology preservation by construct. However, while many deep learning registration methods encourage these properties via loss functions, no earlier methods enforce all of them by construct. Here, we propose a novel registration architecture based on extracting multi-resolution feature representations which is by construct symmetric, inverse consistent, and topology preserving. We also develop an implicit layer for memory efficient inversion of the deformation fields. Our method achieves state-of-the-art registration accuracy on two datasets.

翻译：深度学习已成为可变形医学图像配准中传统迭代方法的有力替代方案，其目标是在两幅图像的坐标系之间建立映射。经典图像配准方法通过构造强制执行对称性、逆一致性和拓扑保持性等有用归纳偏置。然而，尽管许多深度学习方法通过损失函数鼓励这些性质，但尚无早期方法通过构造实现全部特性。本文提出了一种基于多分辨率特征提取的新型配准架构，该架构通过构造实现了对称性、逆一致性和拓扑保持性。我们还开发了一种隐式层以高效存储形变场的逆变换。本方法在两个数据集上实现了最先进的配准精度。