Retinal Vessel Segmentation based on Fully Convolutional Networks

Published in

arXiv.org(2019)

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TL;DR

This work proposes a method for retinal vessel segmentation based on fully convolutional networks that shows superior performance compared to recent state-of-the-art methods.

Abstract

The morphological attributes of retinal vessels, such as length, width, tortuosity and branching pattern and angles, play an important role in diagnosis, screening, treatment, and evaluation of various cardiovascular and ophthalmologic diseases such as diabetes, hypertension and arteriosclerosis. The crucial step before extracting these morphological characteristics of retinal vessels from retinal fundus images is vessel segmentation. In this work, we propose a method for retinal vessel segmentation based on fully convolutional networks. Thousands of patches are extracted from each retinal image and then fed into the network, and data argumentation is applied by rotating extracted patches. Two architectures of fully convolutional networks, U-Net and LadderNet, are used for vessel segmentation. The performance of our method is evaluated on three public datasets: DRIVE, STARE, and CHASE\_DB1. Experimental results of our method show superior performance compared to recent state-of-the-art methods.

References25 items

End-to-End Boundary Aware Networks for Medical Image Segmentation

Deep Active Lesion Segmentation

Deep Dilated Convolutional Nets for the Automatic Segmentation of Retinal Vessels

Retinal vessel segmentation based on Fully Convolutional Neural Networks

M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments

Retinal Vessel Segmentation based on Fully Convolutional Networks

Published in

arXiv.org(2019)

External Links:

Generate Graph

TL;DR

This work proposes a method for retinal vessel segmentation based on fully convolutional networks that shows superior performance compared to recent state-of-the-art methods.

Abstract

References25 items

End-to-End Boundary Aware Networks for Medical Image Segmentation

Deep Active Lesion Segmentation

Deep Dilated Convolutional Nets for the Automatic Segmentation of Retinal Vessels

Retinal vessel segmentation based on Fully Convolutional Neural Networks

M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments

LadderNet: Multi-path networks based on U-Net for medical image segmentation

Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network

Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field

Densely Connected Convolutional Networks

Segmenting Retinal Blood Vessels With Deep Neural Networks

Deep Residual Learning for Image Recognition

U-Net: Convolutional Networks for Biomedical Image Segmentation

Fully convolutional networks for semantic segmentation

Blood vessel segmentation methodologies in retinal images - A survey

Measuring retinal vessel tortuosity in 10-year-old children: validation of the Computer-Assisted Image Analysis of the Retina (CAIAR) program.

Comparative study of retinal vessel segmentation methods on a new publicly available database

Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response

Adaptive histogram equalization and its variations

A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images

Retinal Vessel Segmentation using Deep Neural Networks

Blood vessel segmentation methodologies in retinal images – A survey

Dropout: a simple way to prevent neural networks from overfitting

Data Mining - Concepts and Techniques

Field of Study

EngineeringComputer Science

Journal Information

Name

ArXiv

Volume

abs/2005.00687

Venue Information

Name

arXiv.org

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URL

https://arxiv.org

Alternate Names

ArXiv