Comparison of Automated Thresholding Algorithms in Optical Coherence Tomography Angiography Image Analysis

Abstract

(1) Background: Calculation of vessel density in optical coherence tomography angiography (OCTA) images with thresholding algorithms varies in clinical routine. The ability to discriminate healthy from diseased eyes based on perfusion of the posterior pole is critical and may depend on the algorithm applied. This study assessed comparability, reliability, and ability in the discrimination of commonly used automated thresholding algorithms. (2) Methods: Vessel density in full retina and choriocapillaris slabs were calculated with five previously published automated thresholding algorithms (Default, Huang, ISODATA, Mean, and Otsu) for healthy and diseased eyes. The algorithms were investigated with LD-F2-analysis for intra-algorithm reliability, agreement, and the ability to discriminate between physiological and pathological conditions. (3) Results: LD-F2-analyses revealed significant differences in estimated vessel densities for the algorithms (p < 0.001). For full retina and choriocapillaris slabs, intra-algorithm values range from excellent to poor, depending on the applied algorithm; the inter-algorithm agreement was low. Discrimination was good for the full retina slabs, but poor when applied to the choriocapillaris slabs. The Mean algorithm demonstrated an overall good performance. (4) Conclusions: Automated threshold algorithms are not interchangeable. The ability for discrimination depends on the analyzed layer. Concerning the full retina slab, all of the five evaluated automated algorithms had an overall good ability for discrimination. When analyzing the choriocapillaris, it might be useful to consider another algorithm.

Keywords: automated thresholding; binarization; image processing; optical coherence tomography angiography (OCTA).

Figure 1

Image processing and vessel density (VD) calculation using the Mean algorithm as an example for the groups control, diabetic retinopathy (DR), age-related macular degeneration (AMD), Uveitis, and retinal vein occlusion (RVO) eyes in full retina angiograms (left), and choriocapillaris angiograms (right). The respective B-scans below show the segmentation for these layers.

Figure 2

Vessel density values calculated with the tested algorithms for control and diseased eyes in full retina angiograms. Subgroups of diabetic retinopathy (DR), age-related macular degeneration (AMD), uveitis, and retinal vein occlusion (RVO) were also considered. Circles: outliers of 1.5 times the interquartile range of quartile 1 or quartile 3; stars: extreme outliers of 2.5 times the interquartile range of quartile 1 or quartile 3, respectively.

Figure 3

Vessel density values calculated with the tested algorithms for control and diseased eyes in choriocapillaris angiograms. Subgroups of diabetic retinopathy (DR), age-related macular degeneration (AMD), uveitis, and retinal vein occlusion (RVO) were also considered. Circles: outliers of 1.5 times the interquartile range of quartile 1 or quartile 3; stars: extreme outliers of 2.5 times the interquartile range of quartile 1 or quartile 3, respectively.

Figure 4

Receiver operating characteristics (ROC) curves for discrimination of diseased eyes from the healthy control group in the full retina slabs. The caption shows the area under the curve (AUC) values and the 95% confidence interval.

Figure 5

Receiver operating characteristics (ROC) curves for discrimination of diseased eyes from the healthy control group in the choriocapillaris slabs. The caption shows the area under the curve (AUC) values and the 95% confidence interval.