Towards standardising retinal OCT angiography image analysis with open-source toolbox OCTAVA

Abstract

Quantitative assessment of retinal microvasculature in optical coherence tomography angiography (OCTA) images is important for studying, diagnosing, monitoring, and guiding the treatment of ocular and systemic diseases. However, the OCTA user community lacks universal and transparent image analysis tools that can be applied to images from a range of OCTA instruments and provide reliable and consistent microvascular metrics from diverse datasets. We present a retinal extension to the OCTA Vascular Analyser (OCTAVA) that addresses the challenges of providing robust, easy-to-use, and transparent analysis of retinal OCTA images. OCTAVA is a user-friendly, open-source toolbox that can analyse retinal OCTA images from various instruments. The toolbox delivers seven microvascular metrics for the whole image or subregions and six metrics characterising the foveal avascular zone. We validate OCTAVA using images collected by four commercial OCTA instruments demonstrating robust performance across datasets from different instruments acquired at different sites from different study cohorts. We show that OCTAVA delivers values for retinal microvascular metrics comparable to the literature and reduces their variation between studies compared to their commercial equivalents. By making OCTAVA publicly available, we aim to expand standardised research and thereby improve the reproducibility of quantitative analysis of retinal microvascular imaging. Such improvements will help to better identify more reliable and sensitive biomarkers of ocular and systemic diseases.

Figure 1

Updated OCTAVA graphical user interface with added metrics for retinal analysis. Coloured boxes indicate user controls for optimising image processing. Red box: the user can modify the image by down-sampling or selecting a subregion of the full image for faster processing or by up-sampling to improve the accuracy of the metrics. Blue box: median filter and Frangi filter for improving segmentation. Green box: choice of the segmentation algorithm. Purple box: controls for batch processing. Yellow box: controls for FAZ segmentation and regional analysis.

Figure 2

Illustration of how OCTA-derived microvascular metrics (top row) and the optimised image (middle and bottom rows) change with the Frangi filter maximum kernel size. Top row presents metrics from all 120 participants, each represented by one line. Red dots indicate the mean value. The mean diameter plot appears to have fewer lines since many of the lines overlap. Bottom row shows magnified portions highlighted by yellow boxes in the middle row. For a maximum kernel size 2, the filter misidentifies some large vessels, instead representing them as two smaller vessels (blue arrow). With a larger kernel size of 6, some of the smaller vessels appear artificially dilated (red arrow).

Figure 3

Illustration of how OCTA-derived microvascular metrics (top row) and skeletonized image (bottom row) change with twig size. Top row presents metrics from all 120 participants, each represented by one line. Red dots indicate the mean value. Yellow arrows indicate examples of vessel structures that notably change in the skeletonized image when the twig size is increased.