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---
license: cc-by-nc-nd-4.0
task_categories:
- image-to-image
---
`xvr-data` contains DICOM/NIfTI versions of the `DeepFluoro` and `Ljubljana` datasets.
Paper: [Rapid patient-specific neural networks for intraoperative X-ray to volume registration](https://huggingface.co/papers/2503.16309)
Code: https://github.com/eigenvivek/xvr
## Citation
If you use either of these datasets, please cite the original papers.
```bibtex
@article{grupp2020automatic,
title={Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration},
author={Grupp, Robert B and Unberath, Mathias and Gao, Cong and Hegeman, Rachel A and Murphy, Ryan J and Alexander, Clayton P and Otake, Yoshito and McArthur, Benjamin A and Armand, Mehran and Taylor, Russell H},
journal={International journal of computer assisted radiology and surgery},
volume={15},
pages={759--769},
year={2020},
publisher={Springer}
}
@article{pernus20133d,
title={3D-2D registration of cerebral angiograms: A method and evaluation on clinical images},
author={Mitrović, Uros˘ and S˘piclin, Z˘iga and Likar, Bos˘tjan and Pernus˘, Franjo},
journal={IEEE transactions on medical imaging},
volume={32},
number={8},
pages={1550--1563},
year={2013},
publisher={IEEE}
}
``` |