TorchIO

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TorchIO is a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of 3D medical images in deep learning, following the design of PyTorch.

It includes multiple intensity and spatial transforms for data augmentation and preprocessing. These 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.

The code is available on GitHub.

See Getting started for installation instructions and a usage overview.

Credits

If you use this library for your research, please cite our preprint: 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:

@article{perez-garcia_torchio_2020,
   title = {{TorchIO}: a {Python} library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning},
   shorttitle = {{TorchIO}},
   url = {http://arxiv.org/abs/2003.04696},
   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},
}

This package has been greatly inspired by NiftyNet.