This repository is the official PyTorch code for the paper 'Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation'.

Download our repo:

git clone https://github.com/lseventeen/RF-UNet.git
cd RF-UNet

Install packages from requirements.txt

pip install -r requirements.txt

Choose a path to create a folder with the dataset name and download datasets DRIVE,CHASEDB1,STARE,CHUAC, and DCA1. Type this in terminal to run the data_process.py file

python data_process.py -dp DATASET_PATH -dn DATASET_NAME

Type this in terminal to run the train.py file

python train.py -dp DATASET_PATH

Type this in terminal to run the test.py file

python test.py -dp DATASET_PATH -wp WEIGHT_FILE_PATH

We have prepared the pre-trained models for both datasets in the folder 'pretrained_weights'. To replicate the results in the paper, directly run the following commands

python test.py -dp DATASET_PATH -wp pretrained_weights/DATASET_NAME

This project is licensed under the MIT License - see the LICENSE file for details