# Retina Vessel Segmentation using an Ensemble of U-Nets

![Example Segmentation](/images/Main.png)

## Overview
This provides a software package to automatically segment vasculature from Retinal Fundus photographs. The model uses an ensemble of 10 U-Nets trained on datasets from DRIVE, CHASEdb, and STARE. 

## Installation
This inference code was tested on Ubuntu 18.04.3 LTS, conda version 4.8.0, python 3.7.7, fastai 1.0.61, cuda 10.2, pytorch 1.5.1 and cadene pretrained models 0.7.4. A full list of dependencies is listed in `environment.yml`. 

Inference can be run on the GPU or CPU, and should work with ~4GB of GPU or CPU RAM. For GPU inference, a CUDA 10 capable GPU is required.

For the model weights to download, Github's large file service must be downloaded and installed: https://git-lfs.github.com/ 

This example is best run in a conda environment:

```bash
git lfs clone https://github.com/vineet1992/Retina-Seg/
cd location_of_repo
conda env create -n Retina_Seg -f environment.yml
conda activate Retina_Seg
python Code/seg_ensemble.py test_images/ test_output/ ../model/UNet_Ensemble 10
```
Dummy image files are provided in `test_images/;`. Weights for the segmentation model are in `model/UNet_Ensemble_[0-9].pth`. 
Output will be written to `test_output/`.

## Acknowledgements
I thank the creators of the DRIVE, CHASEdb, STARE databases along with the UK Biobank for access to their datasets.