---
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}
}
```