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.

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Abstract. Arteriolar-to-venular diameter ratio (AVR) is an important clinical measurement that allows to characterize retinal vascular abnormalities. A reliable AVR measurement requires accurate and reproducible width measurement. However, in order to measure the vessel width automatically, an approximation of the intensity profile is required by fitting a model. The aim of the proposed study is to assess the uncertainties introduced in the vessel width measurements when choosing a specific distribution as an intensity profile model. Different models are described and an automatic vessel width measurement procedure is presented. The uncertainty introduced by each model is evaluated by computing the standard deviation of the difference between the automatic and the manual measurements. The results show that the intensity profile model should be chosen according to the relative width of the targeted vessels.

Fantin Girard, Conrad Kavalec, Farida Cheriet, "Statistical atlas-based descriptor for an early detection of optic disc abnormalities," J. Med. Imag. 5(1), 014006 (2018), doi: 10.1117/1.JMI.5.1.014006. Abstract. Optic disc (OD) appearance in fundus images is one of the clinical indicators considered in the assessment of retinal diseases such as glaucoma. The cup-to-disc ratio (CDR) is the most common clinical measurement used to characterize glaucoma. However, the CDR only evaluates the relative sizes of the cup and the OD via their diameters. We propose to construct an atlas-based shape descriptor (ASD) to statistically characterize the geometric deformations of the OD shape and of the blood vessels' configuration inside the OD region. A local representation of the OD region is proposed to construct a well-defined statistical atlas using nonlinear registration and statistical analysis of deformation fields. The shape descriptor is defined as being composed of several statistical measures from the atlas. Analysis of the average model and its principal modes of deformation are performed on a healthy population. The components of the ASD show a significant difference between pathological and healthy ODs. We show that the ASD is able to characterize healthy and glaucomatous OD regions. The deviation map extracted from the atlas can be used to assist clinicians in an early detection of deformation abnormalities in the OD region.