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:https://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