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Tools for loading, augmenting and writing 3D medical images on PyTorch.

Project description


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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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 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.


If you like this repository, please click on Star!

If you use this package for your research, please cite the paper:

Pérez-García et al., 2020, TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning.

BibTeX entry:

    title = {{TorchIO}: a {Python} library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning},
    shorttitle = {{TorchIO}},
    url = {},
    urldate = {2020-03-11},
    journal = {arXiv:2003.04696 [cs, eess, stat]},
    author = {P{\'e}rez-Garc{\'i}a, Fernando and Sparks, Rachel and Ourselin, Sebastien},
    month = mar,
    year = {2020},
    note = {arXiv: 2003.04696},
    keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning},

Getting started

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

Longer usage examples can be found in the notebooks.

All the documentation is hosted on Read the Docs.

Please open a new issue if you think something is missing.


Thanks goes to all these people (emoji key):

Fernando Pérez-García

💻 📖


🤔 👀 💻


💻 👀 🤔



Niels Schurink


Ibrahim Hadzic




Julian Klug


David Völgyes

🤔 💻

Jean-Christophe Fillion-Robin


Suraj Pai


Ben Darwin


Oeslle Lucena


Soumick Chatterjee




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


0.17.0 (23-06-2020)

  • Add transforms history to Subject attributes to improve traceability
  • Add support to use an initial transformation in Resample
  • Add support to use an image file as target in Resample
  • Add mean argument to RandomNoise
  • Add tensor support for transforms
  • Add support to use strings as interpolation argument
  • Add support for 2D images
  • Add attribute access to Subject and Image
  • Add MNI and 3D Slicer datasets
  • Add intensity argument to RandomGhosting
  • Add translation argument to RandomAffine
  • Add shape, spacing and orientation attributes to Image and Subject
  • Refactor samplers
  • Refactor inference classes
  • Add 3D Slicer extension
  • Add ITK-SNAP datasets
  • Add support to take NumPy arrays as transforms input
  • Optimize cropping using PyTorch
  • Optimizing transforms by reducing number of tensor copying
  • Improve representation (repr()) of Image
  • Use lazy loading in Image

0.16.0 (21-04-2020)

  • Add advanced padding options for RandomAffine
  • Add reference space options in Resample
  • Add probability argument to all transforms
  • Add OneOf and Compose transforms to improve composability

0.15.0 (07-04-2020)

  • Refactor RandomElasticDeformation transform
  • Make Subject inherit from dict

0.14.0 (31-03-2020)

  • Add datasets module
  • Add support for DICOM files
  • Add documentation
  • Add CropOrPad transform

0.13.0 (24-02-2020)

  • Add Subject class
  • Add random blur transform
  • Add lambda transform
  • Add random patches swapping transform
  • Add MRI k-space ghosting artefact augmentation

0.12.0 (21-01-2020)

  • Add ToCanonical transform
  • Add CenterCropOrPad transform

0.11.0 (15-01-2020)

  • Add Resample transform

0.10.0 (15-01-2020)

  • Add Pad transform
  • Add Crop transform

0.9.0 (14-01-2020)

  • Add CLI tool to transform an image from file

0.8.0 (11-01-2020)

  • Add Image class

0.7.0 (02-01-2020)

  • Make transforms use PyTorch tensors consistently

0.6.0 (02-01-2020)

  • Add support for NRRD

0.5.0 (01-01-2020)

  • Add bias field transform

0.4.0 (29-12-2019)

  • Add MRI k-space motion artefact augmentation

0.3.0 (21-12-2019)

  • Add Rescale transform
  • Add support for multimodal data and missing modalities

0.2.0 (2019-12-06)

  • First release on PyPI.

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