Method for quantitative assessment of retinal vessel tortuosity in optical coherence tomography angiography applied to sickle cell retinopathy

Abstract

Tortuosity is an important geometric vessel parameter and among the first microvascular alterations observed in various retinopathies. In the current study, a quantitative vessel tortuosity index (VTI) based on a combination of local and global centerline features is presented. Performance of VTI and previously established tortuosity indices were compared against human observers' evaluation of tortuosity. An image-processing pipeline was developed for application of VTI in retinal vessels imaged by optical coherence tomography angiography (OCTA) in perifoveal (6 mm × 6 mm) and parafoveal (3 mm × 3 mm) regions centered on the fovea. Forty-one subjects (12 healthy control (NC) and 29 sickle cell retinopathy (SCR)) and 10 subjects (5 NC and 5 SCR) were imaged in the perifoveal and parafoveal regions, respectively. The relationship between VTI and age was examined in the perifoveal regions in NC subjects. VTI was measured from the OCTA images and compared between NC and SCR subjects using generalized least square regression with and without adjusting for age and race. VTI was found to correlate better than the 4 previous indices with performance of human observers. In the perifoveal region, a significant correlation was observed between VTI and age (r = -0.4, P<0.001, N = 12). VTI was higher in SCR than NC subjects in perifoveal and parafoveal regions (P≤0.001). The results demonstrate that the proposed method shows promise for detection of increased tortuosity in vessels due to retinal disorders.

Optimization-based vessel segmentation pipeline for robust quantification of capillary networks in skin with optical coherence tomography angiography

“…For illustration, exemplary results are shown for tiles of 100 × 100 px (resolution: 6.5 μm). Results using the methods of Liew et al, 22 Khansari et al, 11 Chu et al, 15 Lozzi et al, 25 and OBVS are shown as segmented vessels as well as skeletons in comparison to the ground truth. background.…”

“…Thus far, previous work in OCTA is mainly applied to images of the retinal 11,15,33 and cerebral vasculature. Both show vessel in low scattering environments.…”

“…Quantitative metrics are, for example, the number of vessels, their diameter, and shape, as well as the density and complexity of the vascular network. 5,11 To derive these metrics from an angiographic image, first, a segmentation is required, which classifies each pixel to represent either a vessel or the background. To obtain such a segmentation map, manual labeling by an expert is the gold standard.…”

“…Such a pipeline usually consists of a preprocessing step, which aims to remove noise and to enhance the contrast between foreground and background, a binarization step that separates the object and background, and a postprocessing step that refines the segmented object boundaries. 5,11,15 In computer vision, it is common to evaluate the results of automatic segmentations with a manual ground-truth segmentation of experienced rater. [16][17][18] The congruence of the ground truth and automatic segmentation can be described by the amount of correctly and falsely classified pixels.…”

“…Unfortunately, methods proposed in literature to segment and analyze vascular networks have not been evaluated for robustness against such artifacts that are common for OCTA imaging of the skin. 5,11,15,22,23 However, with OCT as a valuable tool for clinicians and scientists, methods for an automated and robust vessel detection are demanded for a reliable quantification.…”