Metadata-Version: 2.1
Name: diffdrrdata
Version: 0.0.1
Summary: Open-source 2D/3D registration datasets and dataloaders for DiffDRR
Home-page: https://github.com/eigenvivek/DiffDRR-Datasets
Author: Vivek Gopalakrishnan
Author-email: vivekg@mit.edu
License: Apache Software License 2.0
Keywords: nbdev jupyter notebook python
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: diffdrr
Requires-Dist: h5py
Provides-Extra: dev

# DiffDRR Datasets


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## Install

``` zsh
pip install diffdrrdata
```

## DiffDRR

`DiffDRR` is an differentiable X-ray renderer used for solving inverse
problems in tomographic imaging. If you find
[`DiffDRR`](https://github.com/eigenvivek/DiffDRR/) useful in your work,
please cite our paper:

    @inproceedings{gopalakrishnan2022fast,
      title={Fast auto-differentiable digitally reconstructed radiographs for solving inverse problems in intraoperative imaging},
      author={Gopalakrishnan, Vivek and Golland, Polina},
      booktitle={Workshop on Clinical Image-Based Procedures},
      pages={1--11},
      year={2022},
      organization={Springer}
    }

## Datasets

We provide APIs to load the following open-source datasets into
`DiffDRR`:

| **Dataset**                                                         | **Anatomy** | **\# of Subjects** | **\# of 2D Images** | **CTs** | **X-rays** | Fiducials |
|---------------------------------------------------------------------|-------------|:------------------:|:-------------------:|:-------:|:----------:|:---------:|
| [`DeepFluoro`](https://github.com/rg2/DeepFluoroLabeling-IPCAI2020) | pelvis      |         6          |         366         |   ✅    |     ✅     |    ❌     |

<!-- | [`Ljubljana`](https://lit.fe.uni-lj.si/en/research/resources/3D-2D-GS-CA/) | neurovasculature   |         10         |         20          |   ✅    |     ✅     |    ✅     | -->

If you use any of these datasets, please cite the original papers.

### `DeepFluoro`

`DeepFluoro` ([**Grupp et al.,
2020**](https://link.springer.com/article/10.1007/s11548-020-02162-7))
provides paired X-ray fluoroscopy images and CT volume of the pelvis.
The data were collected from six cadaveric subjects at John Hopkins
University. Ground truth camera poses were estimated with an offline
registration process. A visualization of the X-ray / CT pairs in the
`DeepFluoro` dataset is available
[here](https://vivekg.dev/DiffDRR-Datasets/renders/deepfluoro.html).

    @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}
    }
