Science Score: 77.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 5 DOI reference(s) in README -
✓Academic publication links
Links to: sciencedirect.com -
✓Committers with academic emails
5 of 57 committers (8.8%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (10.5%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
Medical imaging processing for AI applications.
Basic Info
- Host: GitHub
- Owner: TorchIO-project
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://docs.torchio.org/
- Size: 44.3 MB
Statistics
- Stars: 2,259
- Watchers: 18
- Forks: 249
- Open Issues: 33
- Releases: 47
Topics
Metadata Files
README.md
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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(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:
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)
- 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.
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):
This project follows the all-contributors specification. Contributions of any kind welcome!
Owner
- Name: TorchIO
- Login: TorchIO-project
- Kind: organization
- Location: United Kingdom
- Website: torchio.org
- Repositories: 2
- Profile: https://github.com/TorchIO-project
TorchIO and related repositories. Created and managed by @fepegar.
Citation (CITATION.cff)
cff-version: 1.2.0
message: If you use this software, please cite the paper using these metadata.
authors:
- family-names: Pérez-García
given-names: Fernando
orcid: https://orcid.org/0000-0001-9090-3024
title: "TorchIO"
repository-code: "https://github.com/TorchIO-project/torchio"
preferred-citation:
type: article
authors:
- family-names: "Pérez-García"
given-names: "Fernando"
orcid: https://orcid.org/0000-0001-9090-3024
- family-names: "Sparks"
given-names: "Rachel"
orcid: https://orcid.org/0000-0003-1553-7903
- family-names: "Ourselin"
given-names: "Sébastien"
orcid: https://orcid.org/0000-0002-5694-5340
doi: "10.1016/j.cmpb.2021.106236"
issn: "0169-2607"
journal: "Computer Methods and Programs in Biomedicine"
pages: "106236"
title: "TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning"
url: "https://www.sciencedirect.com/science/article/pii/S0169260721003102"
year: 2021
GitHub Events
Total
- Create event: 75
- Issues event: 32
- Release event: 15
- Watch event: 133
- Delete event: 47
- Issue comment event: 118
- Push event: 196
- Pull request review event: 39
- Pull request review comment event: 49
- Pull request event: 133
- Fork event: 13
Last Year
- Create event: 75
- Issues event: 32
- Release event: 15
- Watch event: 133
- Delete event: 47
- Issue comment event: 118
- Push event: 196
- Pull request review event: 39
- Pull request review comment event: 49
- Pull request event: 133
- Fork event: 13
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Fernando Perez-Garcia | f****r@g****m | 1,452 |
| allcontributors[bot] | 4****] | 103 |
| pre-commit-ci[bot] | 6****] | 43 |
| dependabot[bot] | 4****] | 15 |
| GFabien | 3****n | 12 |
| David Völgyes | d****s@i****g | 5 |
| nicoloesch | 7****h | 4 |
| Derk Mus | d****s@g****m | 4 |
| Matthew T. Warkentin | m****n@m****a | 4 |
| Cory Efird | c****1@g****m | 3 |
| Julian Klug | t****e@g****m | 3 |
| Justus Schock | 1****k | 3 |
| Sarthak Pati | s****i@p****u | 3 |
| valabregue | r****e@u****r | 3 |
| Niels Schurink | s****s@h****m | 2 |
| deepsource-autofix[bot] | 6****] | 2 |
| ramonemiliani93 | r****i@u****a | 2 |
| siahuat0727 | t****t@g****m | 2 |
| Gustav Müller-Franzes | 5****s | 2 |
| Luca Lumetti | l****a@g****m | 2 |
| G.Reguig | g****g@g****m | 2 |
| Blake Dewey | b****y@j****u | 1 |
| Amund Vedal | 2****l | 1 |
| Amin Alam | m****a@g****m | 1 |
| Albans98 | a****f@o****r | 1 |
| Akis Linardos | l****s@g****m | 1 |
| DaGuT | m****1@g****m | 1 |
| David Kucher | m****r | 1 |
| Ikko Ashimine | e****r@g****m | 1 |
| Chris Winder | 5****r | 1 |
| and 27 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 92
- Total pull requests: 214
- Average time to close issues: 5 months
- Average time to close pull requests: 11 days
- Total issue authors: 31
- Total pull request authors: 22
- Average comments per issue: 2.91
- Average comments per pull request: 0.83
- Merged pull requests: 159
- Bot issues: 0
- Bot pull requests: 43
Past Year
- Issues: 19
- Pull requests: 166
- Average time to close issues: 25 days
- Average time to close pull requests: 4 days
- Issue authors: 16
- Pull request authors: 13
- Average comments per issue: 2.16
- Average comments per pull request: 0.64
- Merged pull requests: 137
- Bot issues: 0
- Bot pull requests: 39
Top Authors
Issue Authors
- fepegar (52)
- romainVala (10)
- HaoLi12345 (2)
- ivezakis (1)
- c-winder (1)
- Jesse-Phitidis (1)
- bcdarwin (1)
- LucaLumetti (1)
- StijnvWijn (1)
- rousseau (1)
- LvanderGoten (1)
- clarkbab (1)
- mueller-franzes (1)
- themantalope (1)
- schomakers (1)
Pull Request Authors
- fepegar (125)
- pyup-bot (16)
- dependabot[bot] (15)
- allcontributors[bot] (15)
- pre-commit-ci[bot] (13)
- Copilot (4)
- romainVala (4)
- jxchen01 (2)
- nicoloesch (2)
- StijnvWijn (2)
- toufiqmusah (2)
- mueller-franzes (2)
- RJacobArthrex (2)
- rickymwalsh (2)
- emmanuel-ferdman (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 2
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Total downloads:
- pypi 87,806 last-month
- Total docker downloads: 73
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Total dependent packages: 20
(may contain duplicates) -
Total dependent repositories: 81
(may contain duplicates) - Total versions: 326
- Total maintainers: 1
pypi.org: torchio
Tools for medical image processing with PyTorch
- Homepage: https://torchio.org
- Documentation: https://docs.torchio.org
- License: Apache Software License
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Latest release: 0.20.22
published 6 months ago
Rankings
Maintainers (1)
conda-forge.org: torchio
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.
- Homepage: https://torchio.readthedocs.io/
- License: Apache-2.0
-
Latest release: 0.18.85
published over 3 years ago