Tools for loading, augmenting and writing 3D medical images on PyTorch.
Project description
TorchIO
🎉 News: the paper is out! 🎉
See the Credits section below for more information.
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.
Documentation
The documentation is hosted on Read the Docs.
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Credits
If you like this repository, please click on Star!
If you use this package for your research, please cite the paper:
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}
}
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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