In the past, optimization-based registration models have used spatially-varying regularization to account for deformation variations in different image regions. However, deep learning-based registration models have mostly relied on spatially-invariant regularization. Here, we introduce an end-to-end framework that uses neural networks to learn a spatially-varying deformation regularizer directly from data. The hyperparameter of the proposed regularizer is conditioned into the network, enabling easy tuning of the regularization strength. The proposed method is built upon a Transformer-based model, but it can be readily adapted to any network architecture. We thoroughly evaluated the proposed approach using publicly available datasets and observed a significant performance improvement while maintaining smooth deformation. The source code of this work will be made available after publication.
@article{arxiv.2303.06168,
title = {Spatially-varying Regularization with Conditional Transformer for Unsupervised Image Registration},
author = {Junyu Chen and Yihao Liu and Yufan He and Yong Du},
journal= {arXiv preprint arXiv:2303.06168},
year = {2023}
}