torchio

Code Tutorials Community

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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

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(Queue for patch-based training)

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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.

Credits

If you like this repository, please click on Star!

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

F. Pérez-García, R. Sparks, and S. Ourselin. TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. Computer Methods and Programs in Biomedicine (June 2021), p. 106236. ISSN: 0169-2607.doi:10.1016/j.cmpb.2021.106236.

BibTeX entry:

@article{perez-garcia_torchio_2021,
    title = {{TorchIO}: a {Python} library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning},
    journal = {Computer Methods and Programs in Biomedicine},
    pages = {106236},
    year = {2021},
    issn = {0169-2607},
    doi = {https://doi.org/10.1016/j.cmpb.2021.106236},
    url = {https://www.sciencedirect.com/science/article/pii/S0169260721003102},
    author = {P{'e}rez-Garc{'i}a, Fernando and Sparks, Rachel and Ourselin, S{'e}bastien},
}

This project is supported by the following institutions:

• Engineering and Physical Sciences Research Council (EPSRC) & UK Research and Innovation (UKRI)
• EPSRC Centre for Doctoral Training in Intelligent, Integrated Imaging In Healthcare (i4health) (University College London)
• Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS) (University College London)
• School of Biomedical Engineering & Imaging Sciences (BMEIS) (King's College London)

Getting started

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

Longer usage examples can be found in the tutorials.

All the documentation is hosted on Read the Docs.

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

Contributors

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 ?
Tyler Spears ?	DaGuT	Xiangyu Zhao ?	siahuat0727 ?	Svdvoort	Albans98	Matthew T. Warkentin
glupol ?	ramonemiliani93 ?	Justus Schock ? ? ?	Stefan Milorad Radonjić ?	Sajan Gohil ?	Ikko Ashimine	laynr
Omar U. Espejel ?	James Butler ?	res191 ?	nengwp ?	susanveraclarke ?	nepersica ?	Sebastian Penhouet ?
Bigsealion ?	Dženan Zukić ?	vasl12 ✅ ?	François Rousseau ?	snavalm	Jacob Reinhold	Hsu ?
snipdome ?	SmallY ?	guigautier ?	AyedSamy ?	J. Miguel Valverde ? ?	José Guilherme Almeida ?	Asim Usman ?
cbri92 ?	Markus J. Ankenbrand ?	Ziv Yaniv	Luca Lumetti	chagelo ?	mueller-franzes ?	Abdelwahab Kawafi ?
Arthur Masson ?	양현식	nicoloesch ?	Amund Vedal	Alabamagan ?	sbdoherty	Zhack47 ?
Blake Dewey	Doyeon Kim ?	KonoMaxi ?	Laurent Chauvin ?	Christian Hinge ?	zzz123xyz ?	Amin Alam
marius-sm ?	haarisr	Chris Winder ?	Ricky Walsh	Keerthi Sravan Ravi ?

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