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

Project description

TorchIO

PyPI downloads PyPI version Google Colab Documentation status Build status Coverage status Code quality Code maintainability Slack


🎉 News: the paper is out! 🎉

See the Credits section below for more information.


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

TorchIO is a Python package containing a set of tools to efficiently read, sample 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.

Credits

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:

@misc{fern2020torchio,
    title={TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning},
    author={Fernando Pérez-García and Rachel Sparks and Sebastien Ourselin},
    year={2020},
    eprint={2003.04696},
    archivePrefix={arXiv},
    primaryClass={eess.IV}
}

Documentation

The documentation is hosted on Read the Docs. It is a work in progress, but some classes such as ImagesDataset are already fairly well documented.

History

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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