Tools like TorchIO are a symptom of the maturation of medical AI research using deep learning techniques.
Jack Clark, Policy Director
at OpenAI (link).
(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 the paper:
BibTeX entry:
@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 project is supported by the Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS) (University College London) and the School of Biomedical Engineering & Imaging Sciences (BMEIS) (King's College London).
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.
Contributors
Thanks goes to all these people (emoji key):
This project follows the
all-contributors
specification. Contributions of any kind welcome!