Deep learning based deformable medical image registration methods have emerged as a strong alternative for classical iterative registration methods. Since image registration is in general an ill-defined problem, the usefulness of inductive biases of symmetricity, inverse consistency and topology preservation has been widely accepted by the research community. However, while many deep learning registration methods enforce these properties via loss functions, no prior deep learning registration method fulfills all of these properties by construct. Here, we propose a novel multi-resolution registration architecture which is by construct symmetric, inverse consistent, and topology preserving. We also develop an implicit layer for memory efficient inversion of the deformation fields. The proposed method achieves state-of-the-art registration accuracy on two datasets.

翻译：基于深度学习的可变形医学图像配准方法已成为经典迭代配准方法的有力替代方案。由于图像配准本质上是一个不适定问题，对称性、逆一致性和拓扑保持等归纳偏置的有效性已被研究界广泛接受。然而，尽管许多深度学习方法通过损失函数施加这些性质，但尚无先前的深度学习方法通过构造满足所有以上性质。本文提出一种新颖的多分辨率配准架构，该架构通过构造实现对称性、逆一致性和拓扑保持性。我们还开发了一种隐式层，用于实现变形场的存储高效反转。所提方法在两个数据集上达到了最先进的配准精度。