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

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

An automatic graph-based method for retinal blood vessel classification

In this paper, we present an automatic approach to classify retinal vessels intoartery and vein classes by analyzing the extracted graph from the vasculature treeand deciding on the type of intersection points (bifurcation, crossing or meetingpoints). The results obtained by the proposed method were compared withmanual classification on 40 images of the INSPIRE-AVR dataset

Retinal Artery/Vein Classification via Graph Cut Optimization

In many diseases with a cardiovascular component, the geometry of microvascular blood vessels changes. These changes are specific to arteries and veins, and can be studied in the microvasculature of the retina using retinal photography. To facilitate large-scale studies of artery/vein-specific changes in the retinal vasculature, automated classification of the vessels is required. Here we present a novel method for artery/vein classification based on local and contextual feature analysis of retinal vessels. For each vessel, local information in the form of a transverse intensity profile is extracted. Crossings and bifurcations of vessels provide contextual information. The local and contextual features are integrated into a non-submodular energy function, which is optimized exactly using graph cuts. The method was validated on a ground truth data set of 150 retinal fundus images, achieving an accuracy of 88.0% for all vessels and 94.0% for the six arteries and six veins with highest caliber in the image

Artery/Vein Vessel Tree Identification in Near-Infrared Reflectance Retinographies

This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10278-019-00235-x[Abstract]: An accurate identification of the retinal arteries and veins is a relevant issue in the development of automatic computer-aided diagnosis systems that facilitate the analysis of different relevant diseases that affect the vascular system as diabetes or hypertension, among others. The proposed method offers a complete analysis of the retinal vascular tree structure by its identification and posterior classification into arteries and veins using optical coherence tomography (OCT) scans. These scans include the near-infrared reflectance retinography images, the ones we used in this work, in combination with the corresponding histological sections. The method, firstly, segments the vessel tree and identifies its characteristic points. Then, Global Intensity-Based Features (GIBS) are used to measure the differences in the intensity profiles between arteries and veins. A k-means clustering classifier employs these features to evaluate the potential of artery/vein identification of the proposed method. Finally, a post-processing stage is applied to correct misclassifications using context information and maximize the performance of the classification process. The methodology was validated using an OCT image dataset retrieved from 46 different patients, where 2,392 vessel segments and 97,294 vessel points were manually labeled by an expert clinician. The method achieved satisfactory results, reaching a best accuracy of 93.35% in the identification of arteries and veins, being the first proposal that faces this issue in this image modality.This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the DTS18/00136 research project and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project. Also, this work has received financial support from the European Union (European Regional Development Fund—ERDF); the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016–2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-04

Volume Rendering of Dense B-Scan Optical Coherence Tomography Angiography to Evaluate the Connectivity of Macular Blood Flow

Purpose: To characterize macular blood flow connectivity using volume rendering of dense B-scan (DB) optical coherence tomography angiography (OCTA) data. Methods: This was a prospective, cross-sectional, observational study. DB OCTA perifoveal scans were performed on healthy subjects using the Spectralis HRA+OCT2. A volumetric projection artifact removal algorithm and customized filters were applied to raw OCTA voxel data. Volume rendering was performed using a workflow on Imaris 9.5 software. Vascular graphs were obtained from angiographic data using the algorithm threshold-loops. Superficial arteries and veins were identified from color fundus photographs and connections between adjacent arteries and veins displayed using the shortest path algorithm. Connective pathway locations were analyzed with cross-sectional OCT and OCTA to determine their course through the superficial vascular complex (SVC) and the deep vascular complex (DVC). Results: Fourteen eyes from seven subjects (mean age: 28 ± 5 years; 3 women) were included in this analysis. One hundred and twenty-six vascular connections were analyzed. In all cases, the shortest path connections between superficial arteries and veins coursed through the DVC. We did not identify shortest path connections confined to the SVC. Conclusions: Volumetric analysis of vascular connectivity supports a predominantly in-series arrangement of blood flow between the SVC and DVC within the human perifoveal macula.publishersversionpublishe

Norrin stimulates cell proliferation in the superficial retinal vascular plexus and is pivotal for the recruitment of mural cells

Mutations in Norrin, the ligand of a receptor complex consisting of FZD4, LRP5 and TSPAN12, cause severe developmental blood vessel defects in the retina and progressive loss of the vascular system in the inner ear, which lead to congenital blindness and progressive hearing loss, respectively. We now examined molecular pathways involved in developmental retinal angiogenesis in a mouse model for Norrie disease. Comparison of morphometric parameters of the superficial retinal vascular plexus (SRVP), including the number of filopodia, vascular density and number of branch points together with inhibition of Notch signaling by using DAPT, suggest no direct link between Norrin and Notch signaling during formation of the SRVP. We noticed extensive vessel crossing within the SRVP, which might be a loss of Wnt- and MAP kinase-characteristic feature. In addition, endomucin was identified as a marker for central filopodia, which were aligned in a thorn-like fashion at P9 in Norrin knockout (Ndpy/−) mice. We also observed elevated mural cell coverage in the SRVP of Ndpy/− mice and explain it by an altered expression of PDGFβ and its receptor (PDGFRβ). In vivo cell proliferation assays revealed a reduced proliferation rate of isolectin B4-positive cells in the SRVP from Ndpy/− mice at postnatal day 6 and a decreased mitogenic activity of mutant compared with the wild-type Norrin. Our results suggest that the delayed outgrowth of the SRVP and decreased angiogenic sprouting in Ndpy/− mice are direct effects of the reduced proliferation of endothelial cells from the SRV