This is the official source code for the paper "$\Upsilon$-Net: A Spatiospectral Network for Retinal OCT Segmentation" accepted in MICCAI 2022.
Authors: A. Farshad*, Y. Yeganeh*, P. Gehlbach, N. Navab
If you find this code useful in your research then please cite:
@inproceedings{farshad2022_ynet,
title={Υ-Net: A Spatiospectral Network for Retinal OCT Segmentation},
author={Farshad, Azade and Yeganeh, Yousef and Gehlbach, Peter and Navab, Nassir},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
year={2022},
organization={Springer}
}
The requirements.txt file includes the required libraries for this project.
python -m venv ynet
source ./ynet/bin/activate
pip install -r requirements.txt
Downloads the dataset, creates the required data directories and preprocesses the data:
sh data_download_and_preprocess.sh
Evaluate the pre-trained models:
python eval.py
This will report the quantitative comparison between UNet and Y-Net + FFC and save the qualitative comparison to "./figs". To compare the model parameters, set --print_params to True.
Train the Y-net + FFC model:
python train.py --dataset [Duke | UMN]
Train the plain Y-net model:
python train.py --model_name y_net_gen --dataset [Duke | UMN]
For UMN, set the number of classes to 2, and the correct data path:
python train.py --dataset UMN --n_classes 2 --image_dir path_to_UMN
Train plain U-Net model:
python train.py --model_name unet --dataset [Duke | UMN]