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Deep learning has emerged as a strong alternative for classical iterativemethods for deformable medical image registration, where the goal is to find amapping between the coordinate systems of two images. Popular classical imageregistration methods enforce the useful inductive biases of symmetricity,inverse consistency, and topology preservation by construct. However, whilemany deep learning registration methods encourage these properties via lossfunctions, none of the methods enforces all of them by construct. Here, wepropose a novel registration architecture based on extracting multi-resolutionfeature representations which is by construct symmetric, inverse consistent,and topology preserving. We also develop an implicit layer for memory efficientinversion of the deformation fields. Our method achieves state-of-the-artregistration accuracy on two datasets.

SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registration