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

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% for vessels segmentation. The A/V classification achieves a specificity of 92.9% with a sensitivity of 93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and 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.Comment: Preprint accepted in Artificial Intelligence in Medicin

An Automatic Method for Assessing Retinal Vessel Width Changes

The Arteriolar-to-Venular Ratio (AVR) is commonly used in studies forthe diagnosis of diseases such as diabetes, hypertension or cardio-vascularpathologies. This paper presents an automatic approach for the estimationof the Arteriolar-to-Venular Ratio (AVR) in retinal images. The proposedmethod includes vessel segmentation, vessel caliber estimation, opticdisc detection, region of interest determination, artery/vein classificationand AVR calculation. The method was assessed using the images ofthe INSPIRE-AVR database. A mean error of 0.05 was obtained when themethods results were compared with reference AVR values provided withthis dataset, thus demonstrating the adequacy of the proposed solution forAVR estimation

Computer-aided diagnosis system for the assessment of retinal vascular changes

This paper presents an automatic application that provides several retinal image analysis functionalities, namely vessel segmentation, vessel width estimation, artery/vein classification and optic disc segmentation. A pipeline of these methods allows the computation of important vessel related indexes, namely the Central Retinal Arteriolar Equivalent (CRAE), Central Retinal Venular Equivalent (CRVE) and Arteriolar-to-Venular Ratio (AVR), as well as various geometrical features associated with vessel bifurcations. The results for AVR estimation were assessed using the images of INSPIRE-AVR dataset; for this dataset, the mean error of the measured AVR values with respect to the reference was identical to the one achieved by a medical expert. The estimation of the CRAE, CRVE and AVR values on 480 images from 120 subjects have shown a significant correlation between right and left eyes and also between images of same eye acquired with different camera fields of view

RetinaCAD - retinal computer-aided diagnosis system

This paper presents an automatic application that provides several retinalimage analysis functionalities, namely vessel segmentation, vesselwidth estimation, artery/vein classification and optic disc segmentation. Apipeline of these methods allows the computation of important vessel relatedindexes, namely the Central Retinal Arteriolar Equivalent (CRAE),Central Retinal Venular Equivalent (CRVE) and Arteriolar-to-Venular Ratio(AVR), as well as various geometrical features associated with vesselbifurcation

Retinal Vascular Network Topology Reconstruction and Artery/Vein Classification via Dominant Set Clustering

The estimation of vascular network topology in complex networks is important in understanding the relationship between vascular changes and a wide spectrum of diseases. Automatic classification of the retinal vascular trees into arteries and veins is of direct assistance to the ophthalmologist in terms of diagnosis and treatment of eye disease. However, it is challenging due to their projective ambiguity and subtle changes in appearance, contrast and geometry in the imaging process. In this paper, we propose a novel method that is capable of making the artery/vein (A/V) distinction in retinal color fundus images based on vascular network topological properties. To this end, we adapt the concept of dominant set clustering and formalize the retinal blood vessel topology estimation and the A/V classification as a pairwise clustering problem. The graph is constructed through image segmentation, skeletonization and identification of significant nodes. The edge weight is defined as the inverse Euclidean distance between its two end points in the feature space of intensity, orientation, curvature, diameter, and entropy. The reconstructed vascular network is classified into arteries and veins based on their intensity and morphology. The proposed approach has been applied to five public databases, INSPIRE, IOSTAR, VICAVR, DRIVE and WIDE, and achieved high accuracies of 95.1%, 94.2%, 93.8%, 91.1%, and 91.0%, respectively. Furthermore, we have made manual annotations of the blood vessel topologies for INSPIRE, IOSTAR, VICAVR, and DRIVE datasets, and these annotations are released for public access so as to facilitate researchers in the community