Source code for our Miccai2023 paper Unsupervised 3D registration through optimization-guided cyclical self-training [pdf].
Please first install the following dependencies
- Python3 (we use 3.9.7)
- pytorch (we use 1.10.2)
- numpy
- scipy
- nibabel
- Download the Abdomen CT-CT dataset of the Learn2Reg challenge.
- Modify the variable path in line 8 of
data_utils.pysuch that it points to the root directory of the data.
Execute python main.py --phase train --out_dir PATH/TO/OUTDIR --gpu GPU --num_warps 2 --ice true --reg_fac 1. --augment true --adam true --sampling true.
In l. 9 of test.py, set the path to the model weights you want to use for testing (for example our final_model.pth). Subsequently, execute python main.py --phase test --gpu GPU
If you find our code useful for your work, please cite the following paper
@article{unsupervised,
title={Unsupervised 3D registration through optimization-guided cyclical self-training},
author={Bigalke, Alexander and Hansen, Lasse and Mok, Tony C. W. and Heinrich, Mattias P},
journal={arXiv preprint arXiv:2306.16997},
year={2023}
}