Unsupervised learning of spatially varying regularization for diffeomorphic image registration
November 27, 2025 ยท View on GitHub
keywords: Deformable Image Registration, Spatially Varying Regularization
This is a PyTorch implementation of my paper:
Updates
11/20/2025 - This work has been accepted for publication in Medical Image Analysis!
Introduction
Spatially varying regularization accommodates the deformation variations that may be necessary for different anatomical regions during deformable image registration. Historically, optimization-based registration models have harnessed spatially varying regularization to address anatomical subtleties. However, most modern deep learning-based models tend to gravitate towards spatially invariant regularization, wherein a homogenous regularization strength is applied across the entire image, potentially disregarding localized variations. In this paper, we propose a hierarchical probabilistic model that integrates a prior distribution on the deformation regularization strength, enabling the end-to-end learning of a spatially varying deformation regularizer directly from the data.
I am in the process of consolidating my image registration work into a single package, MIR. All future updates will be released there rather than across separate repositories like this one.
Registration tasks
- Atlas-to-subject registration on IXI dataset (brain MRI) [code | See MIR Package]
- Atlas-to-subject registration on AutoPET dataset (whole-body CT) [See MIR Package]
- Intra-subject registration on ACDC and M&Ms datasets (cardiac MRI) [See MIR Package]
- Inter-subject registration on 4DCT dataset (Lung CT) [See MIR Package]
Examples of the learned spatially varying regularizer
Hypernetwork for continuous regularization control
We further incorporated the concept from HyperMorph, enabling the learning of a set of regularization hyperparameters for continuous control of spatially varying regularization at the test time.
Citation:
If you find this code is useful in your research, please consider to cite:
@article{chen2025unsupervised,
title={Unsupervised learning of spatially varying regularization for diffeomorphic image registration},
author={Chen, Junyu and Wei, Shuwen and Liu, Yihao and Bian, Zhangxing and He, Yufan and Carass, Aaron and Bai, Harrison and Du, Yong},
journal={Medical Image Analysis},
pages={103887},
year={2025},
publisher={Elsevier}
}