https://github.com/fepegar/torchio

Medical imaging toolkit for deep learning
https://github.com/fepegar/torchio

augmentation data-augmentation deep-learning machine-learning medical-image-analysis medical-image-computing medical-image-processing medical-images medical-imaging-datasets medical-imaging-with-deep-learning python pytorch

Last synced: 3 days ago
JSON representation

Medical imaging toolkit for deep learning

• Host: GitHub
• URL: https://github.com/fepegar/torchio
• Owner: fepegar
• License: apache-2.0
• Created: 2019-11-26T09:10:09.000Z (about 5 years ago)
• Default Branch: main
• Last Pushed: 2024-10-28T16:39:42.000Z (about 2 months ago)
• Last Synced: 2024-10-29T11:31:32.938Z (about 1 month ago)
• Topics: augmentation, data-augmentation, deep-learning, machine-learning, medical-image-analysis, medical-image-computing, medical-image-processing, medical-images, medical-imaging-datasets, medical-imaging-with-deep-learning, python, pytorch
• Language: Python
• Homepage: https://torchio.org
• Size: 44.2 MB
• Stars: 2,070
• Watchers: 19
• Forks: 241
• Open Issues: 42
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Citation: CITATION.cff

Awesome Lists containing this project

• awesome_medical - torchio

README

        



  

    

  



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

Jack Clark, Policy Director

at [OpenAI](https://openai.com/) ([link](https://jack-clark.net/2020/03/17/)).

---

    

        

            Package

        

        

            

                

            

            

                

            

            

                

            

        

    

    

        

            CI

        

        

            

                

            

            

                

            

            

                

            

        

    

    

        

            Code

        

        

            

                

            

            

                

            

            

                

            

            

                

            

        

    

    

        

            Tutorials

        

        

            

                

            

        

    

    

        

            Community

        

        

            

                

            

            

                

            

            

                

            

            

                

            

        

    

---



  

    

  





  

    

  



---

    

        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](https://torchio.readthedocs.io/patches/patch_training.html#queue)

for [patch-based training](https://torchio.readthedocs.io/patches/index.html))

---

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](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](https://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,

[which is not actively maintained anymore](https://github.com/NifTK/NiftyNet/commit/935bf4334cd00fa9f9d50f6a95ddcbfdde4031e0).

## 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.](https://doi.org/10.1016/j.cmpb.2021.106236)

BibTeX entry:

```bibtex

@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)](https://epsrc.ukri.org/)

- [EPSRC Centre for Doctoral Training in Intelligent, Integrated Imaging In Healthcare (i4health)](https://www.ucl.ac.uk/intelligent-imaging-healthcare/) (University College London)

- [Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS)](https://www.ucl.ac.uk/interventional-surgical-sciences/) (University College London)

- [School of Biomedical Engineering & Imaging Sciences (BMEIS)](https://www.kcl.ac.uk/bmeis) (King's College London)

## Getting started

See [Getting started](https://torchio.readthedocs.io/quickstart.html) for

[installation](https://torchio.readthedocs.io/quickstart.html#installation)

instructions

and a [Hello, World!](https://torchio.readthedocs.io/quickstart.html#hello-world)

example.

Longer usage examples can be found in the

[tutorials](https://github.com/fepegar/torchio/blob/main/tutorials/README.md).

All the documentation is hosted on

[Read the Docs](http://torchio.rtfd.io/).

Please

[open a new issue](https://github.com/fepegar/torchio/issues/new/choose)

if you think something is missing.

## Contributors

Thanks goes to all these people ([emoji key](https://allcontributors.org/docs/en/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](https://github.com/all-contributors/all-contributors)

specification. Contributions of any kind welcome!