scikit-surgerytorch 0.2.4

scikit-surgerytorch is a Python package

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

• Source code repository
• Documentation

Licensing and copyright

Copyright 2020 University College London. scikit-surgerytorch is released under the BSD-3 license. Please see the license file for details.

Acknowledgements

Supported by Wellcome and EPSRC.