Abstract
In this paper we present a fully automated graph-based segmentation algorithm that jointly uses optical coherence tomography (OCT) and OCT angiography (OCTA) data to segment Bruch's membrane (BM). This is especially valuable in cases where the spatial correlation between BM, which is usually not visible on OCT scans, and the retinal pigment epithelium (RPE), which is often used as a surrogate for segmenting BM, is distorted by pathology. We validated the performance of our proposed algorithm against manual segmentation in a total of 18 eyes from healthy controls and patients with diabetic retinopathy (DR), non-exudative age-related macular degeneration (AMD) (early/intermediate AMD, nascent geographic atrophy (nGA) and drusen-associated geographic atrophy (DAGA) and geographic atrophy (GA)), and choroidal neovascularization (CNV) with a mean absolute error of ∼0.91 pixel (∼4.1 μm). This paper suggests that OCT-OCTA segmentation may be a useful framework to complement the growing usage of OCTA in ophthalmic research and clinical communities.
Highlights
Optical coherence tomography (OCT) is a well established imaging modality in ophthalmology
In this paper we presented an OCT-OCT angiography (OCTA) segmentation approach for segmenting Bruch’s membrane (BM) in the presence of pathology
The motivation to use both structure (OCT) and blood flow (OCTA) information arose from observations about the retinal pigment epithelium (RPE)-BM-CC complex, which was altered in pathology, and how these alterations were reflected on OCT and OCTA imaging
Summary
Optical coherence tomography (OCT) is a well established imaging modality in ophthalmology. Because of the eye’s curvature, and because any given retinal layer is curved itself, simple “re-slicing” of the volume along the plane orthogonal to the optical axis cannot guarantee the proper visualization of the ocular vascular network. Such re-slicing will result in an en face image that contains different retinal layers as a function of lateral position. For this reason, segmentation of different ocular layers is a necessary prerequisite for many types of OCTA interpretation and analysis. Manual tracing remains the gold standard for segmentation, but the large data sizes of OCT(A) volumes make it impractically laborious in most settings, necessitating the usage of automatic algorithms
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