Graphene-U-Net is a library that offers simple and easy to use functions for training and evaluating Neural Networks for Segmentation of microscopy images using the U-Net Architecture. The library is based on an implementation of U-Net in the PyTorch deep Llarning framework, and uses OpenCV/Numpy for the data handling as well as Scikit-learn for the evaluation metrics. It contains functions for loading the dataset, training using k fold cross validation, and inferring the network on new data.
Robbie Sadre, Colin Ophus, Anastasiia Butko, Gunther Weber (Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720)
See LICENSE.txt for licensing information.
Graphene U-Net Copyright (c) 2021, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.
If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov.
NOTICE. This Software was developed under funding from the U.S. Dept. of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.
This code has been verified using:
- Python 3.7.6 / 3.8.5
- PyTorch-gpu 1.4.0 / 1.7.1
- OpenCV 4.4.0 / 4.5.1
- scikit-learn 0.22.1 / 0.23.2
- NumPy 1.18.1 / 1.19.2
- Pandas 1.0.0 / 1.1.5
This code uses jvanvugt/pytorch-unet, copyright (c) 2018 Joris.
git clone https://github.com/lbnlcomputerarch/graphene-u-net.git
In terminal:
cd graphene-u-net/
jupyter notebook usage.ipynb
If you use this for research, please cite the original paper:
Sadre, R., Ophus, C., Butko, A., & Weber, G. (2021). Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase-Contrast Transmission Electron Microscopy Images. Microscopy and Microanalysis, 1-11. doi:10.1017/S1431927621000167
@article{
sadre_ophus_butko_weber_2021,
title={Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase-Contrast Transmission Electron Microscopy Images},
DOI={10.1017/S1431927621000167}, journal={Microscopy and Microanalysis},
publisher={Cambridge University Press},
author={Sadre, Robbie and Ophus, Colin and Butko, Anastasiia and Weber, Gunther H.},
year={2021},
pages={1–11}
}