scikit-surgerytorch
Author: Thomas Dowrick
scikit-surgerytorch is part of the scikit-surgery software project, developed at the Wellcome EPSRC Centre for Interventional and Surgical Sciences, part of University College London (UCL).
The aim of scikit-surgery torch is to provide a home for various pytorch models/examples/utilities that may be useful for Image Guided Surgery.
Features
Implemented models:
- High Resolution Stereo network Inference only, see author's repo for pre trained weights. As at commit aae0b9b.
- Volume2SurfaceCNN Inferencece only, see author's repo for pre trained weights. As at commit 5a656381.
- Models can run on GPU or CPU.
- Example usage in tests/.
scikit-surgerytorch is NOT meant to be a layer on-top of pytorch
or provide a new kind-of platform. The aim is that researchers can learn from examples,
and importantly, learn how to deliver an algorithm that can be used by other people
out of the box, with just a `pip install`, rather than a new user having to
re-implement stuff, or struggle to get someone else's code running.
Cloning
You can clone the repository using the following command:
git clone https://github.com/UCL/scikit-surgerytorch
Running tests
Pytest is used for running unit tests:
pip install pytest python -m pytest
Linting
This code conforms to the PEP8 standard. Pylint can be used to analyse the code:
pip install pylint pylint --rcfile=tests/pylintrc sksurgerytorch
Installing
You can pip install directly from the repository as follows:
pip install git+https://github.com/UCL/scikit-surgerytorch
Contributing
Please see the contributing guidelines.
Useful links
Licensing and copyright
Copyright 2020 University College London. scikit-surgerytorch is released under the BSD-3 license. Please see the license file for details.
