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PyTorch implementation of HighRes3DNet

Project description

highresnet

https://img.shields.io/pypi/v/highresnet.svg https://img.shields.io/travis/fepegar/highresnet.svg https://zenodo.org/badge/DOI/10.5281/zenodo.3349989.svg Documentation Status Updates
$ NII_FILE=`download_oasis`
$ deepgif $NII_FILE
3D Slicer screenshot

PyTorch implementation of HighRes3DNet from Li et al. 2017, *On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task*.

All the information about how the weights were ported from NiftyNet can be found in my submission to the MICCAI Educational Challenge 2019.

Usage

Command line interface

(deepgif) $ deepgif t1_mri.nii.gz
Using cache found in /home/fernando/.cache/torch/hub/fepegar_highresnet_master
100%|███████████████████████████████████████████| 36/36 [01:13<00:00,  2.05s/it]

PyTorch Hub

If you are using pytorch>=1.1.0, you can import the model directly from this repository using PyTorch Hub.

>>> import torch
>>> repo = 'fepegar/highresnet'
>>> model_name = 'highres3dnet'
>>> print(torch.hub.help(repo, model_name))
    "HighRes3DNet by Li et al. 2017 for T1-MRI brain parcellation"
    "pretrained (bool): load parameters from pretrained model"
>>> model = torch.hub.load(repo, model_name, pretrained=True)
>>>

Installation

2. Install PyTorch and highresnet

Within the conda environment:

pip install pytorch highresnet

Now you can do

>>> from highresnet import HighRes3DNet
>>> model = HighRes3DNet(in_channels=1, out_channels=160)
>>>

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.7.1 (2019-11-05)

  • First release on PyPI.

Project details


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