Metadata-Version: 2.1
Name: torchio
Version: 0.13.16
Summary: Tools for loading, augmenting and writing 3D medical images on PyTorch.
Home-page: https://github.com/fepegar/torchio
Author: Fernando Perez-Garcia
Author-email: fernando.perezgarcia.17@ucl.ac.uk
License: MIT license
Keywords: torchio
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: Click (>=7.0)
Requires-Dist: nibabel
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: SimpleITK
Requires-Dist: torch (>=1.2)
Requires-Dist: torchvision
Requires-Dist: tqdm

# [TorchIO](http://torchio.rtfd.io/)

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---

### 🎉 News: the paper is out! 🎉

See the [Credits](#credits) section below for more information.

---

<table align="center">
    <tr>
        <td align="center">Original</td>
        <td align="center">Random blur</td>
    </tr>
    <tr>
        <td align="center"><img src="docs/images/gifs_readme/1_Lambda_mri.png" alt="Original"></td>
        <td align="center"><img src="docs/images/gifs_readme/2_RandomBlur_mri.gif" alt="Random blur"></td>
    </tr>
    <tr>
        <td align="center">Random flip</td>
        <td align="center">Random noise</td>
    </tr>
    <tr>
        <td align="center"><img src="docs/images/gifs_readme/3_RandomFlip_mri.gif" alt="Random flip"></td>
        <td align="center"><img src="docs/images/gifs_readme/4_Compose_mri.gif" alt="Random noise"></td>
    </tr>
    <tr>
        <td align="center">Random affine transformation</td>
        <td align="center">Random elastic transformation</td>
    </tr>
    <tr>
        <td align="center"><img src="docs/images/gifs_readme/5_RandomAffine_mri.gif" alt="Random affine transformation"></td>
        <td align="center"><img src="docs/images/gifs_readme/6_RandomElasticDeformation_mri.gif" alt="Random elastic transformation"></td>
    </tr>
    <tr>
        <td align="center">Random bias field artifact</td>
        <td align="center">Random motion artifact</td>
    </tr>
    <tr>
        <td align="center"><img src="docs/images/gifs_readme/7_RandomBiasField_mri.gif" alt="Random bias field artifact"></td>
        <td align="center"><img src="docs/images/gifs_readme/8_RandomMotion_mri.gif" alt="Random motion artifact"></td>
    </tr>
    <tr>
        <td align="center">Random spike artifact</td>
        <td align="center">Random ghosting artifact</td>
    </tr>
    <tr>
        <td align="center"><img src="docs/images/gifs_readme/9_RandomSpike_mri.gif" alt="Random spike artifact"></td>
        <td align="center"><img src="docs/images/gifs_readme/10_RandomGhosting_mri.gif" alt="Random ghosting artifact"></td>
    </tr>
</table>



TorchIO is a Python package containing a set of tools to efficiently
read, sample and write 3D medical images in deep learning applications
written in [PyTorch](https://pytorch.org/),
including intensity and spatial transforms
for data augmentation and preprocessing. Transforms include typical computer vision operations
such as random affine transformations and also domain-specific ones such as
simulation of intensity artifacts due to
[MRI magnetic field inhomogeneity](http://mriquestions.com/why-homogeneity.html)
or [k-space motion artifacts](http://proceedings.mlr.press/v102/shaw19a.html).

This package has been greatly inspired by [NiftyNet](https://niftynet.io/).


## Credits

If you like this repository, please click on Star!

If you use this package for your research, please cite the paper:

[Pérez-García et al., 2020, *TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning*](https://arxiv.org/abs/2003.04696).


BibTeX entry:

```bibtex
@misc{fern2020torchio,
    title={TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning},
    author={Fernando Pérez-García and Rachel Sparks and Sebastien Ourselin},
    year={2020},
    eprint={2003.04696},
    archivePrefix={arXiv},
    primaryClass={eess.IV}
}
```


## [Documentation](https://torchio.readthedocs.io/)

The documentation is hosted on
[Read the Docs](https://torchio.readthedocs.io/).
It is a work in progress, but some classes such as
[`ImagesDataset`](https://torchio.readthedocs.io/data/image.html)
are already fairly well documented.


=======
History
=======


0.13.0 (24-02-2020)
-------------------

* Add Subject class
* Add random blur transform
* Add lambda transform
* Add random patches swapping transform
* Add MRI k-space ghosting artefact augmentation


0.12.0 (21-01-2020)
-------------------

* Add ToCanonical transform
* Add CenterCropOrPad transform


0.11.0 (15-01-2020)
-------------------

* Add Resample transform


0.10.0 (15-01-2020)
-------------------

* Add Pad transform
* Add Crop transform


0.9.0 (14-01-2020)
------------------

* Add CLI tool to transform an image from file


0.8.0 (11-01-2020)
------------------

* Add Image class


0.7.0 (02-01-2020)
------------------

* Make transforms use PyTorch tensors consistently


0.6.0 (02-01-2020)
------------------

* Add support for NRRD


0.5.0 (01-01-2020)
------------------

* Add bias field transform


0.4.0 (29-12-2019)
------------------

* Add MRI k-space motion artefact augmentation


0.3.0 (21-12-2019)
------------------

* Add Rescale transform
* Add support for multimodal data and missing modalities


0.2.0 (2019-12-06)
------------------

* First release on PyPI.


