Skip to main content

Medical image preprocessing, augmentation, and patch-based training.

Project description

TorchIO logo

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

Jack Clark, Policy Director at OpenAI, Co-Founder and Head of Policy of Anthropic (link).


Package PyPI downloads PyPI version Conda version
CI Tests status Documentation status Coverage status
Code Code style Code quality
Tutorials Google Colab
Community YouTube All Contributors

Progressive artifacts

Augmentation


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

Queue

(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.

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 was originally supported by the following institutions:

Getting started

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

Longer usage examples can be found in the tutorials.

Read the documentation for more information.

Please create an issue if you think something is missing.

Contributors

Thanks goes to all these people (emoji key):

Fernando Pérez-García
Fernando Pérez-García

💻 📖
valabregue
valabregue

🤔 👀 💻 💬 🐛
GFabien
GFabien

💻 👀 🤔
G.Reguig
G.Reguig

💻
Niels Schurink
Niels Schurink

💻
Ibrahim Hadzic
Ibrahim Hadzic

🐛
ReubenDo
ReubenDo

🤔
Julian Klug
Julian Klug

🤔
David Völgyes
David Völgyes

🤔 💻
Jean-Christophe Fillion-Robin
Jean-Christophe Fillion-Robin

📖
Suraj Pai
Suraj Pai

🤔
Ben Darwin
Ben Darwin

🤔
Oeslle Lucena
Oeslle Lucena

🐛
Soumick Chatterjee
Soumick Chatterjee

💻
neuronflow
neuronflow

📖
Jan Witowski
Jan Witowski

📖
Derk Mus
Derk Mus

📖 💻 🐛
Christian Herz
Christian Herz

🐛
Cory Efird
Cory Efird

💻 🐛
Esteban Vaca C.
Esteban Vaca C.

🐛
Ray Phan
Ray Phan

🐛
Akis Linardos
Akis Linardos

🐛 💻
Nina Montana-Brown
Nina Montana-Brown

📖 🚇
fabien-brulport
fabien-brulport

🐛
malteekj
malteekj

🐛
Andres Diaz-Pinto
Andres Diaz-Pinto

🐛
Sarthak Pati
Sarthak Pati

📦 📖
GabriellaKamlish
GabriellaKamlish

🐛
Tyler Spears
Tyler Spears

🐛
DaGuT
DaGuT

📖
Xiangyu Zhao
Xiangyu Zhao

🐛
siahuat0727
siahuat0727

📖 🐛
Svdvoort
Svdvoort

💻
Albans98
Albans98

💻
Matthew T. Warkentin
Matthew T. Warkentin

💻
glupol
glupol

🐛
ramonemiliani93
ramonemiliani93

📖 🐛 💻
Justus Schock
Justus Schock

💻 🐛 🤔 👀
Stefan Milorad Radonjić
Stefan Milorad Radonjić

🐛
Sajan Gohil
Sajan Gohil

🐛
Ikko Ashimine
Ikko Ashimine

📖
laynr
laynr

📖
Omar U. Espejel
Omar U. Espejel

🔊
James Butler
James Butler

🐛
res191
res191

🔍
nengwp
nengwp

🐛 📖
susanveraclarke
susanveraclarke

🎨
nepersica
nepersica

🐛
Sebastian Penhouet
Sebastian Penhouet

🤔
Bigsealion
Bigsealion

🐛
Dženan Zukić
Dženan Zukić

👀
vasl12
vasl12

🐛
François Rousseau
François Rousseau

🐛
snavalm
snavalm

💻
Jacob Reinhold
Jacob Reinhold

💻
Hsu
Hsu

🐛
snipdome
snipdome

🐛
SmallY
SmallY

🐛
guigautier
guigautier

🤔
AyedSamy
AyedSamy

🐛
J. Miguel Valverde
J. Miguel Valverde

🤔 💻 🐛
José Guilherme Almeida
José Guilherme Almeida

🤔
Asim Usman
Asim Usman

🐛
cbri92
cbri92

🐛
Markus J. Ankenbrand
Markus J. Ankenbrand

🐛
Ziv Yaniv
Ziv Yaniv

📖
Luca Lumetti
Luca Lumetti

💻 📖
chagelo
chagelo

🐛
mueller-franzes
mueller-franzes

💻 🐛
Abdelwahab Kawafi
Abdelwahab Kawafi

🐛
Arthur Masson
Arthur Masson

🐛 📖
양현식
양현식

💻
nicoloesch
nicoloesch

💻 🐛 🎨 🚧 💬 👀
Amund Vedal
Amund Vedal

📖
Alabamagan
Alabamagan

🐛
sbdoherty
sbdoherty

📖
Zhack47
Zhack47

🐛
Blake Dewey
Blake Dewey

📖
Doyeon Kim
Doyeon Kim

🐛
KonoMaxi
KonoMaxi

🐛
Laurent Chauvin
Laurent Chauvin

🐛
Christian Hinge
Christian Hinge

🐛
zzz123xyz
zzz123xyz

🐛
Amin Alam
Amin Alam

📖
marius-sm
marius-sm

🤔
haarisr
haarisr

💻
Chris Winder
Chris Winder

🐛
Ricky Walsh
Ricky Walsh

💻
Keerthi Sravan Ravi
Keerthi Sravan Ravi

🐛
Rahul Kurian Jacob
Rahul Kurian Jacob

📖
Ethan Rooke
Ethan Rooke

📖
David Kucher
David Kucher

💻
StijnvWijn
StijnvWijn

💻 🐛
Toufiq
Toufiq

💻
bcrobo
bcrobo

🐛
Emmanuel Ferdman
Emmanuel Ferdman

🐛
Anders Dahl Henriksen
Anders Dahl Henriksen

🚧
Eike Petersen
Eike Petersen

🐛
Vincent Gao
Vincent Gao

🐛 💻
Mengyuan Ding
Mengyuan Ding

🐛

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

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

torchio-2.0.0a2.tar.gz (179.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

torchio-2.0.0a2-py3-none-any.whl (220.5 kB view details)

Uploaded Python 3

File details

Details for the file torchio-2.0.0a2.tar.gz.

File metadata

  • Download URL: torchio-2.0.0a2.tar.gz
  • Upload date:
  • Size: 179.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.11.29 {"installer":{"name":"uv","version":"0.11.29","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for torchio-2.0.0a2.tar.gz
Algorithm Hash digest
SHA256 13a0357ed49f86f96fe8f300823b687e578d1aac73781d3ebf52ce670cb79e58
MD5 d08f5995f9ec40a5ace52d268a5e6d1f
BLAKE2b-256 54c32b346a27a3be95574a321e74802b02cc7bb69dae737bf06c7f5adbc0e762

See more details on using hashes here.

Provenance

The following attestation bundles were made for torchio-2.0.0a2.tar.gz:

Publisher: publish.yml on TorchIO-project/torchio

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file torchio-2.0.0a2-py3-none-any.whl.

File metadata

  • Download URL: torchio-2.0.0a2-py3-none-any.whl
  • Upload date:
  • Size: 220.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.11.29 {"installer":{"name":"uv","version":"0.11.29","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for torchio-2.0.0a2-py3-none-any.whl
Algorithm Hash digest
SHA256 261756227f78226d1e75f2898f5bfa0544947f327ea9da0e93e7b6ea94c2e649
MD5 45dcd02f85cf63ed5d79d04dfe13a708
BLAKE2b-256 f6cdd93e300ef4296685e7c11ec30fcd7881018bd1321750f90d3db18d354c62

See more details on using hashes here.

Provenance

The following attestation bundles were made for torchio-2.0.0a2-py3-none-any.whl:

Publisher: publish.yml on TorchIO-project/torchio

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page