Retinal vessel segmentation based on GT-UNet

Kent0n

Last update: Dec 18, 2022

Related tags

Deep Learning GT-U-Net

Overview

Retinal vessel segmentation based on GT-UNet

Introduction

This project is a retinal blood vessel segmentation code based on UNet-like Group Transformer Network (GT-UNet), including data preprocessing, model training and testing, visualization, etc.

Requirements

The main package and version of the python environment are as follows

# Name                    Version         
python                    3.7.9                    
pytorch                   1.7.0         
torchvision               0.8.0         
cudatoolkit               10.2.89       
cudnn                     7.6.5           
matplotlib                3.3.2              
numpy                     1.19.2        
opencv                    3.4.2         
pandas                    1.1.3        
pillow                    8.0.1         
scikit-learn              0.23.2          
scipy                     1.5.2           
tensorboardX              2.1        
tqdm                      4.54.1

Usage

The project structure and intention are as follows :

VesselSeg-Pytorch			# Source code		
    ├── config.py		 	# Configuration information
    ├── lib			            # Function library
    │   ├── common.py
    │   ├── dataset.py		        # Dataset class to load training data
    │   ├── datasetV2.py		        # Dataset class to load training data with lower memory
    │   ├── extract_patches.py		# Extract training and test samples
    │   ├── help_functions.py		# 
    │   ├── __init__.py
    │   ├── logger.py 		        # To create log
    │   ├── losses
    │   ├── metrics.py		        # Evaluation metrics
    │   └── pre_processing.py		# Data preprocessing
    ├── models		        # All models are created in this folder
    │   ├── __init__.py
    │   ├── nn
    │   └── GT-UNet.py
    ├── prepare_dataset	        # Prepare the dataset (organize the image path of the dataset)
    │   ├── chasedb1.py
    │   ├── data_path_list		  # image path of dataset
    │   ├── drive.py
    │   └── stare.py
    ├── tools			     # some tools
    │   ├── ablation_plot.py
    │   ├── ablation_plot_with_detail.py
    │   ├── merge_k-flod_plot.py
    │   └── visualization
    ├── function.py			        # Creating dataloader, training and validation functions 
    ├── test.py			            # Test file
    └── train.py			          # Train file

Training model

Please confirm the configuration information in the config.py. Pay special attention to the train_data_path_list and test_data_path_list. Then, running:

python train.py

You can configure the training information in config, or modify the configuration parameters using the command line. The training results will be saved to the corresponding directory(save name) in the experiments folder.

3) Testing model

The test process also needs to specify parameters in config.py. You can also modify the parameters through the command line, running:

python test.py

The above command loads the best_model.pth in ./experiments/GT-UNet_vessel_seg and performs a performance test on the testset, and its test results are saved in the same folder.