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 construction. However, while many deep learning registration methods encourage these properties via loss functions, no earlier methods enforce all of them by construction. Here, we propose a novel registration architecture based on extracting multi-resolution feature representations which is by construction 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 three datasets. The code is available at https://github.com/honkamj/SITReg.

翻译：深度学习已成为经典迭代方法在可变形医学图像配准中的有力替代方案，其目标是在两幅图像的坐标系之间建立映射。经典的图像配准方法通过构造方式强制实现了对称性、逆一致性和拓扑保持性这些有用的归纳偏置。然而，尽管许多深度学习配准方法通过损失函数来鼓励这些特性，但此前尚无方法在构造上同时强制实现所有这些特性。本文提出了一种新颖的配准架构，该架构基于提取多分辨率特征表示，其在构造上天然具有对称性、逆一致性和拓扑保持性。我们还开发了一种隐式层，用于实现变形场的内存高效求逆。我们的方法在三个数据集上达到了最先进的配准精度。代码可在 https://github.com/honkamj/SITReg 获取。