Keras/Tensorflow implementation of coSegGAN. This work has been published in Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (https://miccai2021.org/en/).
Python 3.6
Tensorflow: 2.0.0
Keras: 2.3.1
Recreate conda environment as follows:
conda env create -f environment.yml
Or if you are using Docker:
docker pull tazleef/tf2.0.0-cv-keras2.3.1-imgaug:latest
Links to the publicly available dataset used in this work:
Due to privacy policy, we are unable to share our surgical dataset. However, we intend to release them soon once we get the approval. For now, we have included a few sample cases for reference. Alternatively, if you have your own data, format it in the same way and set the filepath and training parameters in data_path_Loader.py
and train.py
.
To train the model, run train.py
.
This code can also be used for other segmentation tasks with domain variations.
If you use this code or the provided model, please cite the following paper:
@inproceedings{kalia2021co,
title={Co-Generation and Segmentation for Generalized Surgical Instrument Segmentation on Unlabelled Data},
author={Kalia, Megha and Aleef, Tajwar Abrar and Navab, Nassir and Black, Peter and Salcudean, Septimiu E},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={403--412},
year={2021},
organization={Springer}
}
}
If you face any problem using this code then create an issue in this repository or contact me at [email protected]
The code is based on https://github.com/eriklindernoren/Keras-GAN/tree/master/cyclegan
MIT