This software is forked from lseventeen/FR-UNet and allows segmentation of blood vessels in OCT reconstruction images of the human eye retina. Details of the application of the software can be found in the paper:
Marciniak, T.; Stankiewicz, A.; Zaradzki, P. Neural Networks Application for Accurate Retina Vessel Segmentation from OCT Fundus Reconstruction. Sensors 2023, 23, 1870. https://doi.org/10.3390/s23041870
Link to the paper: https://www.mdpi.com/1424-8220/23/4/1870
The dataset CAVRI-C used by the software is available free of charge at: http://dsp.org.pl/CAVRI_Database/191/
Example of three fundus reconstructions with ground truth and corresponding segmentation results for 5 neural networks (analyzed in the paper above):
Here is the original readme.md from https://github.com/lseventeen/FR-UNet with environment requirements and setup information.
This repository is the official PyTorch code for the paper 'Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation' (Wentao Liu, Huihua Yang, Tong Tian, Zhiwei Cao, Xipeng Pan, Weijin Xu and Yang Jin)
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