junyuchen245/Correlation_Ratio

Correlation Ratio for multi- and mono-modal image registration (PyTorch)

Correlation Ratio for Multi- and Mono-modal Image Registration

keywords: correlation ratio, image registration, multi-modal image registration

This repository hosts PyTorch implementation of Correlation Ratio for medical image registration, originally proposed in this paper. We have evaluated CR has a loss function for the application of both affine and deformable registration.

1. Affine Registration: Chen, Junyu, et al. "Unsupervised Learning of Multi-modal Affine Registration for PET/CT,” 2024 IEEE NSS/MIC
2. To be added...

You can find the PyTorch implementation of the correlation ratio and local-patch-based correlation ratio here:

• correlation ratio
• local correlation ratio

PET/CT Multi-Modal Affine Registration

The source code for PET/CT affine registration can be found here, and you will need to install the required packages listed in the requirements.txt file.

Multi-scale Instance-specific Optimization

Qualitative Results

T1/T2 Brain MRI Multi-Modal Deformable Registration

To be added...

Citation

If you find this code is useful in your research, please consider to cite:

@misc{chen2024unsupervised,
  title={Unsupervised Learning of Multi-modal Affine Registration for PET/CT}, 
  author={Junyu Chen and Yihao Liu and Shuwen Wei and Aaron Carass and Yong Du},
  year={2024},
  eprint={2409.13863},
  archivePrefix={arXiv},
  primaryClass={eess.IV},
  url={https://arxiv.org/abs/2409.13863}, 
}