An implementation of the research paper "Retina Blood Vessel Segmentation Using A U-Net Based Convolutional Neural Network"

Overview

Retina Blood Vessels Segmentation

This is an implementation of the research paper "Retina Blood Vessel Segmentation Using A U-Net Based Convolutional Neural Network" written by Wang Xiancheng, Li Weia, et al.

Check out the standalone demo notebook and run segRetino inferences here.

Open In Colab

Inspiration

Various eye diseases can be diagnosed through the characterization of the retinal blood vessels. The characterization can be extracted by using proper imaging techniques and data analysis methods. In case of eye examination, one of the important tasks is the retinal image segmentation.The paper presents a network and training strategy that relies on the data augmentation to use the available annotated samples more efficiently, to segment retinal blood vessels using a UNET convolutional neural network.

Dataset

We have used the Digital Retinal Images for Vessel Extraction (DRIVE) dataset for retinal vessel segmentation. It consists of a total of JPEG 40 color fundus images; including 7 abnormal pathology cases. Each image resolution is 584x565 pixels with eight bits per color channel (3 channels), resized to 512x512 for our model.

Guidelines to download, setup and use the dataset

The DRIVE dataset may be downloaded here as two files named training.zip and test.zip.

Please write the following commands on your terminal to extract the file in the proper directory.

  $ mkdir drive
  $ unzip </path/to/training.zip> -d </path/to/drive>
  $ unzip </path/to/test.zip> -d </path/to/drive>

The resulting directory structure should be:

/path/to/drive
    -> train
        -> image
            -> 21_training_0.tif
            -> 22_training_0.tif
               ...
        -> mask
            -> 21_training_0.gif
            -> 22_training_0.gif
    -> test
        -> image
            -> 01_test_0.tif
            -> 02_test_0.tif
               ...
        -> mask
            -> 01_test_0.gif
            -> 02_test_0.gif

Model Components

The UNET CNN architecture may be divided into the Encoder, Bottleneck and Decoder blocks, followed by a final segmentation output layer.

  • Encoder: There are 4 Encoder blocks, each consisting of a convolutional block followed by a Spatial Max Pooling layer.
  • Bottleneck: The Bottleneck consists of a single convolutional block.
  • Decoder: There are 4 Decoder blocks, each consisting of a deconvolution operation, followed by a convolutional block, along with skip connections.

Note: The convolutional block consists of 2 conv2d operations each followed by a BatchNorm2d, finally followed by a ReLU activation.

model_arch

Implementation Details

  • Image preprocessing included augmentations like HorizontalFlip, VerticalFlip, Rotate.
  • Dataloader object was created for both training and validation data
  • Training process was carried out for 50 epochs, using the Adam Optimizer with a Learning Rate 1e-4.
  • Validation was carried out using Dice Loss and Intersection over Union Loss.

Installation and Quick Start

To use the repo and run inferences, please follow the guidelines below

  • Cloning the Repository:

      $ git clone https://github.com/srijarkoroy/segRetino
    
  • Entering the directory:

      $ cd segRetino/
    
  • Setting up the Python Environment with dependencies:

      $ pip install -r requirements.txt
    
  • Running the file for inference:

      $ python3 test.py
    

Running the test file downloads the pretrained weights of the UNET Model that we have trained on the DRIVE Dataset. However if you want to re-train the model please mention the path to your dataset on you local machine after augmentations, inside the train.py file, as:

train_x = sorted(glob(<path/to/augmented/train/image/folder/>))
train_y = sorted(glob(<path/to/augmented/mask/image/folder/>))

valid_x = sorted(glob(<path/to/test/image/folder/>))
valid_y = sorted(glob(<path/to/test/mask/folder/>))

Once the path has been mentioned, the model may be trained by running the command:

  $ python3 train.py

Note: If images have not been augmented, please see the instructions for augmentation here.

The test file saves two images in the mentioned paths, a masked image showing only the blood vessels, and a blend image showing the blood vessels within the retina. If you don't want to save the blend image, consider running the following code snippet:

# Creating the SegRetino object initialized with the test image path
seg = SegRetino('<path/to/test/img>')

# Running inference
seg.inference(set_weight_dir = 'unet.pth', path = '<path/to/save/masked/image>', blend=False, blend_path = None)

Check out the standalone demo notebook and run segRetino inferences here.

Note: Is is advisable to use a GPU for running the inferences since performing segmentation on 512x512 images with a heavy UNET architecture is expensive.

Results from Implementation

Original Image Masked Image Blend Image

Contributors

Contribution

Contributions are always welcome! Please check out this doc for Contribution Guidelines.

Owner
Srijarko Roy
AI Enthusiast!
Srijarko Roy
A Closer Look at Structured Pruning for Neural Network Compression

A Closer Look at Structured Pruning for Neural Network Compression Code used to reproduce experiments in https://arxiv.org/abs/1810.04622. To prune, w

Bayesian and Neural Systems Group 140 Dec 05, 2022
[CVPR 2022] Official code for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration"

MDCA Calibration This is the official PyTorch implementation for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved

MDCA Calibration 21 Dec 22, 2022
Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"

Optimization as a Model for Few-Shot Learning This repo provides a Pytorch implementation for the Optimization as a Model for Few-Shot Learning paper.

Albert Berenguel Centeno 238 Jan 04, 2023
Official repository of IMPROVING DEEP IMAGE MATTING VIA LOCAL SMOOTHNESS ASSUMPTION.

IMPROVING DEEP IMAGE MATTING VIA LOCAL SMOOTHNESS ASSUMPTION This is the official repository of IMPROVING DEEP IMAGE MATTING VIA LOCAL SMOOTHNESS ASSU

电线杆 14 Dec 15, 2022
Make Watson Assistant send messages to your Discord Server

Make Watson Assistant send messages to your Discord Server Prerequisites Sign up for an IBM Cloud account. Fill in the required information and press

1 Jan 10, 2022
Explore extreme compression for pre-trained language models

Code for paper "Exploring extreme parameter compression for pre-trained language models ICLR2022"

twinkle 16 Nov 14, 2022
Simple reference implementation of GraphSAGE.

Reference PyTorch GraphSAGE Implementation Author: William L. Hamilton Basic reference PyTorch implementation of GraphSAGE. This reference implementat

William L Hamilton 861 Jan 06, 2023
K-Means Clustering and Hierarchical Clustering Unsupervised Learning Solution in Python3.

Unsupervised Learning - K-Means Clustering and Hierarchical Clustering - The Heritage Foundation's Economic Freedom Index Analysis 2019 - By David Sal

David Salako 1 Jan 12, 2022
Multivariate Time Series Forecasting with efficient Transformers. Code for the paper "Long-Range Transformers for Dynamic Spatiotemporal Forecasting."

Spacetimeformer Multivariate Forecasting This repository contains the code for the paper, "Long-Range Transformers for Dynamic Spatiotemporal Forecast

QData 440 Jan 02, 2023
The Official TensorFlow Implementation for SPatchGAN (ICCV2021)

SPatchGAN: Official TensorFlow Implementation Paper "SPatchGAN: A Statistical Feature Based Discriminator for Unsupervised Image-to-Image Translation"

39 Dec 30, 2022
A distributed deep learning framework that supports flexible parallelization strategies.

FlexFlow FlexFlow is a deep learning framework that accelerates distributed DNN training by automatically searching for efficient parallelization stra

528 Dec 25, 2022
IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID,

Intermediate Domain Module (IDM) This repository is the official implementation for IDM: An Intermediate Domain Module for Domain Adaptive Person Re-I

Yongxing Dai 87 Nov 22, 2022
PyTorch Lightning implementation of Automatic Speech Recognition

lasr Lightening Automatic Speech Recognition An MIT License ASR research library, built on PyTorch-Lightning, for developing end-to-end ASR models. In

Soohwan Kim 40 Sep 19, 2022
Multiple paper open-source codes of the Microsoft Research Asia DKI group

📫 Paper Code Collection (MSRA DKI Group) This repo hosts multiple open-source codes of the Microsoft Research Asia DKI Group. You could find the corr

Microsoft 249 Jan 08, 2023
Official PyTorch implementation of Spatial Dependency Networks.

Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling Đorđe Miladinović   Aleksandar Stanić   Stefan Bauer   Jürgen Schmid

Djordje Miladinovic 34 Jan 19, 2022
Detecting Blurred Ground-based Sky/Cloud Images

Detecting Blurred Ground-based Sky/Cloud Images With the spirit of reproducible research, this repository contains all the codes required to produce t

1 Oct 20, 2021
Official implementation of the paper "Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering"

Light Field Networks Project Page | Paper | Data | Pretrained Models Vincent Sitzmann*, Semon Rezchikov*, William Freeman, Joshua Tenenbaum, Frédo Dur

Vincent Sitzmann 130 Dec 29, 2022
Pseudo-Visual Speech Denoising

Pseudo-Visual Speech Denoising This code is for our paper titled: Visual Speech Enhancement Without A Real Visual Stream published at WACV 2021. Autho

Sindhu 94 Oct 22, 2022
REGTR: End-to-end Point Cloud Correspondences with Transformers

REGTR: End-to-end Point Cloud Correspondences with Transformers This repository contains the source code for REGTR. REGTR utilizes multiple transforme

Zi Jian Yew 108 Dec 17, 2022
System-oriented IR evaluations are limited to rather abstract understandings of real user behavior

Validating Simulations of User Query Variants This repository contains the scripts of the experiments and evaluations, simulated queries, as well as t

IR Group at Technische Hochschule Köln 2 Nov 23, 2022