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An implementation of《Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks》

Python 100.00%

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Vessel-wgan-pytorch

Author: Yuchao Gu

E-mail: [email protected]

Date: 2018-05-27

Description: The code is an pytorch implementation of 《Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks》

Overview

Data

DRIVE: Digital Retinal Images for Vessel Extraction you can download the train and test data from this server. You can also find data in the eyedata folder.

Pre-processing

The dataset contains 20 training images, the first step of my pre-processing is randomly cropping into 512*512. The second step is to randomly change brightness ,contrast and hue of the train image. I implement this method in my code, so you can be convenient to use it. Further more, a gan-based method of generating retina images can be used as an extra data source.

Model

Training

python train.py

How to use

Dependencies

This code depends on the following libraries:

Python 3.6
Pytorch
PIL

structure

vessel gan
│
├── eyedata  # drive data
│ 
├── gycutils # my utils for data augmentation
│ 
├── Criterion.py # generate and store precison,recall curve 
│ 
├── datasets.py # dataset for dataloader
│ 
├── gan.py # generative adversial network for vessel segmentation
│ 
├── train.py # train code
│
├── transform.py 
│
└── readme.md # introduce to this project

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