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arXiv · 2303.06168

Spatially-varying Regularization with Conditional Transformer for Unsupervised Image Registration

Junyu Chen·Yihao Liu·Yufan He·Yong Du
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Abstract

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.

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Junyu Chen, Yihao Liu, Yufan He, Yong Du. 2023-03-10. Spatially-varying Regularization with Conditional Transformer for Unsupervised Image Registration. https://arxiv.org/abs/2303.06168

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