Skip to main content

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

Project description

TorchIO

Tools like TorchIO are a symptom of the maturation of medical AI research using deep learning techniques.

Jack Clark, Policy Director at OpenAI (link).


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


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

(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.

Documentation

The documentation is hosted on Read the Docs.

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

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}
}

Contributors ✨

Thanks goes to these wonderful people (emoji key):


Fernando Pérez-García

💻 📖

valabregue

🤔 👀 💻

GFabien

💻

G.Reguig

💻

Niels Schurink

💻

Ibrahim Hadzic

🐛

ReubenDo

🤔

Julian Klug

🤔

David Völgyes

🤔

Jean-Christophe Fillion-Robin

📖

Suraj Pai

🤔

Ben Darwin

🤔

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

History

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.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

torchio-0.16.12.tar.gz (23.1 MB view details)

Uploaded Source

Built Distribution

torchio-0.16.12-py2.py3-none-any.whl (77.2 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file torchio-0.16.12.tar.gz.

File metadata

  • Download URL: torchio-0.16.12.tar.gz
  • Upload date:
  • Size: 23.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.4.0 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.7.1

File hashes

Hashes for torchio-0.16.12.tar.gz
Algorithm Hash digest
SHA256 6fc839c1e50704bd196b998045563a9e643225e87b52fbed80469f5a429877af
MD5 434d128d625e8eb4b985fdd9068519bc
BLAKE2b-256 d1a1a71b1cd63b931d906fbacb6d4b80cf7957be23536d24a1f34718d8b40af6

See more details on using hashes here.

File details

Details for the file torchio-0.16.12-py2.py3-none-any.whl.

File metadata

  • Download URL: torchio-0.16.12-py2.py3-none-any.whl
  • Upload date:
  • Size: 77.2 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.4.0 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.7.1

File hashes

Hashes for torchio-0.16.12-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 e850a6fff1949e84ed7b9566e5bcbad5cfb0266812ea25bd3a933d5f730d63ca
MD5 4e3f00aaa5a7edf8ea2a798daa51a54b
BLAKE2b-256 f86678565e59cad9e387865a814d748483626bb731e2b0ef15971e9da7682457

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page