SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registration
This addresses the need for reliable and efficient deformable image registration in medical imaging, with incremental improvements in enforcing key properties.
The paper tackled the problem of ensuring symmetric, inverse consistent, and topology preserving properties in deep learning-based medical image registration, achieving state-of-the-art accuracy on three datasets.
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.