torchio

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Medical imaging processing for AI applications.

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About

Medical imaging processing for AI applications.

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

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        <a href="https://pypi.org/project/torchio/">
            
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        <a href="https://anaconda.org/conda-forge/torchio">
            
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        <b>CI</b>
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        <a href="https://github.com/TorchIO-project/torchio/actions/workflows/docs.yml">
            
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        <a href="https://app.codecov.io/github/TorchIO-project/torchio">
            
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        <b>Code</b>
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        <a href="https://docs.astral.sh/ruff/">
            
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        <a href="https://scrutinizer-ci.com/g/TorchIO-project/torchio/?branch=main">
            
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        <b>Tutorials</b>
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        <b>Community</b>
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        <a href="https://github.com/TorchIO-project/torchio#contributors">
            
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Original Random blur

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        <a href="https://docs.torchio.org/transforms/augmentation.html#randomaffine">Random affine transformation</a>
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        <a href="https://docs.torchio.org/transforms/augmentation.html#randomelasticdeformation">Random elastic transformation</a>
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        <a href="https://docs.torchio.org/transforms/augmentation.html#randombiasfield">Random bias field artifact</a>
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        <a href="https://docs.torchio.org/transforms/augmentation.html#randommotion">Random motion artifact</a>
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        <a href="https://docs.torchio.org/transforms/augmentation.html#randomspike">Random spike artifact</a>
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        <a href="https://docs.torchio.org/transforms/augmentation.html#randomghosting">Random ghosting artifact</a>
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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:

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:

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 was originally supported by the following institutions: