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, none of the methods enforces 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经典迭代方法的强有力替代方案，其目标是找到两幅图像坐标系之间的映射。经典图像配准方法通过构建过程强制引入对称性、逆一致性和拓扑保持等有效归纳偏置。然而，尽管许多深度学习方法通过损失函数鼓励这些特性，但没有方法通过构建过程强制执行所有特性。在此，我们提出一种基于多分辨率特征表示提取的新型配准架构，该架构通过构建过程天然具有对称性、逆一致性和拓扑保持特性。我们还开发了一种隐式层，用于形变场的存储高效逆变换。我们的方法在两个数据集上实现了最先进的配准精度。