Tram-Line filtering for retinal vessel segmentation

The segmentation of the vascular network from retinal fundal images is a fundamental step in the analysis of the retina, and may be used for a number of purposes, including diagnosis of diabetic retinopathy. However, due to the variability of retinal images segmentation is difficult, particularly with images of diseased retina which include significant distractors. This paper introduces a non-linear filter for vascular segmentation, which is particularly robust against such distractors. We demonstrate results on the publicly-available STARE dataset, superior to Stare’s performance, with 57.2% of the vascular network (by length) successfully located, with 97.2% positive predictive value measured by vessel length, compared with 57% and 92.2% for Stare. The filter is also simple and computationally efficient

A fluid-dynamic based approach to reconnect the retinal vessels in fundus photography

This paper introduces the use of fluid-dynamic modeling to determine the connectivity of overlapping venous and arterial vessels in fundus images. Analysis of the retinal vascular network may provide information related to systemic and local disorders. However, the automated identification of the vascular trees in retinal images is a challenging task due to the low signal-to-noise ratio, nonuniform illumination and the fact that fundus photography is a projection on to the imaging plane of three-dimensional retinal tissue. A zero-dimensional model was created to estimate the hemodynamic status of candidate tree configurations. Simulated annealing was used to search for an optimal configuration. Experimental results indicate that simulated annealing was very efficient on test cases that range from small to medium size networks, while ineffective on large networks. Although for large networks the nonconvexity of the cost function and the large solution space made searching for the optimal solution difficult, the accuracy (average success rate = 98.35%), and simplicity of our novel approach demonstrate its potential effectiveness in segmenting retinal vascular trees

Three-dimensional Imaging Coupled with Topological Quantification Uncovers Retinal Vascular Plexuses Undergoing Obliteration

Introduction: Murine models provide microvascular insights into the 3-D network disarray seen in retinopathy and cardiovascular diseases. Light-sheet fluorescence microscopy (LSFM) has emerged to capture retinal vasculature in 3-D, allowing for assessment of the progression of retinopathy and the potential to screen new therapeutic targets in mice. We hereby coupled LSFM, also known as selective plane illumination microscopy, with topological quantification, to characterize the retinal vascular plexuses undergoing preferential obliteration. Method and Result: In postnatal mice, we revealed the 3-D retinal microvascular network in which the vertical sprouts bridge the primary (inner) and secondary (outer) plexuses, whereas, in an oxygen-induced retinopathy (OIR) mouse model, we demonstrated preferential obliteration of the secondary plexus and bridging vessels with a relatively unscathed primary plexus. Using clustering coefficients and Euler numbers, we computed the local versus global vascular connectivity. While local connectivity was preserved (p > 0.05, n = 5 vs. normoxia), the global vascular connectivity in hyperoxia-exposed retinas was significantly reduced (p < 0.05, n = 5 vs. normoxia). Applying principal component analysis (PCA) for auto-segmentation of the vertical sprouts, we corroborated the obliteration of the vertical sprouts bridging the secondary plexuses, as evidenced by impaired vascular branching and connectivity, and reduction in vessel volumes and lengths (p < 0.05, n = 5 vs. normoxia). Conclusion: Coupling 3-D LSFM with topological quantification uncovered the retinal vasculature undergoing hyperoxia-induced obliteration from the secondary (outer) plexus to the vertical sprouts. The use of clustering coefficients, Euler's number, and PCA provided new network insights into OIR-associated vascular obliteration, with translational significance for investigating therapeutic interventions to prevent visual impairment

Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models

The health and function of tissue rely on its vasculature network to provide reliable blood perfusion. Volumetric imaging approaches, such as multiphoton microscopy, are able to generate detailed 3D images of blood vessels that could contribute to our understanding of the role of vascular structure in normal physiology and in disease mechanisms. The segmentation of vessels, a core image analysis problem, is a bottleneck that has prevented the systematic comparison of 3D vascular architecture across experimental populations. We explored the use of convolutional neural networks to segment 3D vessels within volumetric in vivo images acquired by multiphoton microscopy. We evaluated different network architectures and machine learning techniques in the context of this segmentation problem. We show that our optimized convolutional neural network architecture, which we call DeepVess, yielded a segmentation accuracy that was better than both the current state-of-the-art and a trained human annotator, while also being orders of magnitude faster. To explore the effects of aging and Alzheimer's disease on capillaries, we applied DeepVess to 3D images of cortical blood vessels in young and old mouse models of Alzheimer's disease and wild type littermates. We found little difference in the distribution of capillary diameter or tortuosity between these groups, but did note a decrease in the number of longer capillary segments ($>75\mu m$) in aged animals as compared to young, in both wild type and Alzheimer's disease mouse models.Comment: 34 pages, 9 figure

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

Automated Segmentation of Optical Coherence Tomography Angiography Images:Benchmark Data and Clinically Relevant Metrics

Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging modality for the visualisation of microvasculature in vivo that has encountered broad adoption in retinal research. OCTA potential in the assessment of pathological conditions and the reproducibility of studies relies on the quality of the image analysis. However, automated segmentation of parafoveal OCTA images is still an open problem. In this study, we generate the first open dataset of retinal parafoveal OCTA images with associated ground truth manual segmentations. Furthermore, we establish a standard for OCTA image segmentation by surveying a broad range of state-of-the-art vessel enhancement and binarisation procedures. We provide the most comprehensive comparison of these methods under a unified framework to date. Our results show that, for the set of images considered, deep learning architectures (U-Net and CS-Net) achieve the best performance. For applications where manually segmented data is not available to retrain these approaches, our findings suggest that optimal oriented flux is the best handcrafted filter from those considered. Furthermore, we report on the importance of preserving network structure in the segmentation to enable deep vascular phenotyping. We introduce new metrics for network structure evaluation in segmented angiograms. Our results demonstrate that segmentation methods with equal Dice score perform very differently in terms of network structure preservation. Moreover, we compare the error in the computation of clinically relevant vascular network metrics (e.g. foveal avascular zone area and vessel density) across segmentation methods. Our results show up to 25% differences in vessel density accuracy depending on the segmentation method employed. These findings should be taken into account when comparing the results of clinical studies and performing meta-analyses

Two-dimensional segmentation of the retinal vascular network from optical coherence tomography

The automatic segmentation of the retinal vascular network from ocular fundus images has been performed by several research groups. Although different approaches have been proposed for traditional imaging modalities, only a few have addressed this problem for optical coherence tomography (OCT). Furthermore, these approaches were focused on the optic nerve head region. Compared to color fundus photography and fluorescein angiography, two-dimensional ocular fundus reference images computed from three-dimensional OCT data present additional problems related to system lateral resolution, image contrast, and noise. Specifically, the combination of system lateral resolution and vessel diameter in the macular region renders the process particularly complex, which might partly explain the focus on the optic disc region. In this report, we describe a set of features computed from standard OCT data of the human macula that are used by a supervised-learning process (support vector machines) to automatically segment the vascular network. For a set of macular OCT scans of healthy subjects and diabetic patients, the proposed method achieves 98% accuracy, 99% specificity, and 83% sensitivity. This method was also tested on OCT data of the optic nerve head region achieving similar results

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