:fire: FireANTs: Adaptive Riemannian Optimization for Multi-Scale Diffeomorphic Registration

The FireANTs library is a lightweight registration package for Riemannian diffeomorphic registration on GPUs.

Installation

To use the FireANTs package, you can either clone the repository and install the package locally or install the package directly from PyPI. We recommend using a fresh Anaconda/Miniconda environment to install the package.

conda create -n fireants python=3.7

To install FireANTs locally:

git clone https://github.com/rohitrango/fireants
cd fireants
pip install -e .

Or install from PyPI:

pip install fireants

Tutorial

To check out some of the tutorials, check out the tutorials/ directory for usage. Alternatively, to reproduce the results in the paper checkout the fireants/scripts/ directory.

Datasets

In the paper, we use the datasets as following:

Klein's evaluation of 14 non-linear registration algorithms: here
EMPIRE10 lung registration challenge: here
Expansion Microscopy dataset: here

Contributing

Feel free to add issues or pull requests to the repository. We welcome contributions to the package.

Citation

If you use FireANTs in your research, please cite the following paper:

@article{jena2024fireants,
  title={FireANTs: Adaptive Riemannian Optimization for Multi-Scale Diffeomorphic Registration},
  author={Jena, Rohit and Chaudhari, Pratik and Gee, James C},
  journal={arXiv preprint arXiv:2404.01249},
  year={2024}
}

rohitrango / FireANTs