Joint segmentation and classification of retinal arteries/veins from fundus images

03/04/2019
by   Fantin Girard, et al.
0

Objective Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. This paper proposes a fast, novel method for semantic A/V segmentation combining deep learning and graph propagation. Methods A convolutional neural network (CNN) is proposed to jointly segment and classify vessels into arteries and veins. The initial CNN labeling is propagated through a graph representation of the retinal vasculature, whose nodes are defined as the vessel branches and edges are weighted by the cost of linking pairs of branches. To efficiently propagate the labels, the graph is simplified into its minimum spanning tree. Results The method achieves an accuracy of 94.8 The A/V classification achieves a specificity of 92.9 93.7 sensitivity, both of 91.7 Conclusion The results show that our method outperforms the leading previous works on a public dataset for A/V classification and is by far the fastest. Significance The proposed global AVR calculated on the whole fundus image using our automatic A/V segmentation method can better track vessel changes associated to diabetic retinopathy than the standard local AVR calculated only around the optic disc.

READ FULL TEXT

page 4

page 9

page 10

research
09/20/2022

Simultaneous segmentation and classification of the retinal arteries and veins from color fundus images

The study of the retinal vasculature is a fundamental stage in the scree...
research
02/04/2022

Fully Automated Tree Topology Estimation and Artery-Vein Classification

We present a fully automatic technique for extracting the retinal vascul...
research
12/14/2018

Unsupervised Change Detection in Satellite Images Using Convolutional Neural Networks

This paper proposes an efficient unsupervised method for detecting relev...
research
04/07/2020

Dense Residual Network for Retinal Vessel Segmentation

Retinal vessel segmentation plays an imaportant role in the field of ret...
research
05/29/2019

Segmentation of blood vessels in retinal fundus images

In recent years, several automatic segmentation methods have been propos...
research
11/10/2022

Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification

Glaucoma is a prevalent cause of blindness worldwide. If not treated pro...
research
06/06/2018

Deep Vessel Segmentation By Learning Graphical Connectivity

We propose a novel deep-learning-based system for vessel segmentation. E...

Please sign up or login with your details

Forgot password? Click here to reset