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