View torchio on Snyk Open Source Advisor

Package Health Score

87 / 100

security
No known security issues
popularity
Popular
maintenance
Healthy
community
Active

Popularity

Popular

GitHub Stars: 2.07K
Forks: 240
Contributors: 60

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package torchio, we found that it has been starred 2,074 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Oct 5, 2024
Direct Vulnerabilities: 0
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0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

Apache-2.0

Security Policy

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Maintenance

Healthy

Commit Frequency

Open Issues: 34
Open PR: 10
Last Release: 1 month ago
Last Commit: 10 days ago

Further analysis of the maintenance status of torchio based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Healthy.

We found that torchio demonstrates a positive version release cadence with at least one new version released in the past 3 months.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Active

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 60
Funding: No

With more than 10 contributors for the torchio repository, this is possibly a sign for a growing and inviting community.

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Package

Python Versions Compatibility: <3.13,>=3.8

Age: 5 years
Latest Release: 1 month ago
Dependencies: 6 Direct
Versions: 253
Maintainers: 1
Wheels: OS Independent

Readme

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

Jack Clark, Policy Director at OpenAI (link).

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 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 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} 💻

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

torchio dependencies

deprecated humanize nibabel scipy tqdm typer

FAQs

What is torchio?

Tools for medical image processing with PyTorch. Visit Snyk Advisor to see a full health score report for torchio, including popularity, security, maintenance & community analysis.

Is torchio popular?

The python package torchio receives a total of 14,545 weekly downloads. As such, torchio popularity was classified as a popular. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is torchio well maintained?

We found that torchio demonstrated a healthy version release cadence and project activity. It has a community of 60 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is torchio safe to use?

The python package torchio was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 9 November-2024, at 11:29 (UTC).

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torchio

Tools for medical image processing with PyTorch For more information about how to use this package see README

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Getting started

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