Tools like TorchIO are a symptom of the maturation of medical AI research using deep learning techniques.

Jack Clark, Policy Director at OpenAI (link).

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Original	Random blur
Random flip	Random noise
Random affine transformation	Random elastic transformation
Random bias field artifact	Random motion artifact
Random spike artifact	Random ghosting artifact

(Queue for patch-based training)

TorchIO is a Python package containing a set of tools to efficiently read, preprocess, sample, augment, and write 3D medical images in deep learning applications written in PyTorch, 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 or k-space motion artifacts.

This package has been greatly inspired by NiftyNet, which is not actively maintained anymore.

If you like this repository, please click on Star!

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

Pérez-García, F., Sparks, R., Ourselin, S.: TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. arXiv:2003.04696 [cs, eess, stat] (Mar 2020), http://arxiv.org/abs/2003.04696, arXiv: 2003.04696

BibTeX entry:

@article{perez-garcia_torchio_2020,
    title = {{TorchIO}: a {Python} library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning},
    shorttitle = {{TorchIO}},
    url = {http://arxiv.org/abs/2003.04696},
    urldate = {2020-03-11},
    journal = {arXiv:2003.04696 [cs, eess, stat]},
    author = {P{\'e}rez-Garc{\'i}a, Fernando and Sparks, Rachel and Ourselin, Sebastien},
    month = mar,
    year = {2020},
    note = {arXiv: 2003.04696},
    keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning},
}

This project is supported by the Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS) (University College London) and the School of Biomedical Engineering & Imaging Sciences (BMEIS) (King's College London).

See Getting started for installation instructions and a Hello, World! example.

Longer usage examples can be found in the notebooks.

All the documentation is hosted on Read the Docs.

Please open a new issue if you think something is missing.

Thanks goes to all these people (emoji key):

Fernando Pérez-García 💻 📖	valabregue 🤔 👀 💻	GFabien 💻 👀 🤔	G.Reguig 💻	Niels Schurink 💻	Ibrahim Hadzic 🐛	ReubenDo 🤔
Julian Klug 🤔	David Völgyes 🤔 💻	Jean-Christophe Fillion-Robin 📖	Suraj Pai 🤔	Ben Darwin 🤔	Oeslle Lucena 🐛	Soumick Chatterjee 💻
neuronflow 📖	Jan Witowski 📖	Derk Mus 📖 💻	Christian Herz 🐛	Cory Efird 💻	Esteban Vaca C. 🐛	Ray Phan 🐛
Akis Linardos 🐛 💻	Nina Montana-Brown 📖	fabien-brulport 🐛	malteekj 🐛	Andres Diaz-Pinto 🐛	Sarthak Pati 📦	GabriellaKamlish 🐛

This project follows the all-contributors specification. Contributions of any kind welcome!