SVC-Net: A spatially vascular connectivity network for deep learning construction of microcapillary angiography from single-scan-volumetric OCT

Abstract

As one modality extension of optical coherence tomography (OCT), OCT angiography (OCTA) provides unparalleled capability for depth-resolved visualization of retinal vasculature at the microcapillary level resolution. For OCTA image construction, repeated OCT scans from one location are required for temporally vascular connectivity (TVC), i.e., OCT signal variance among sequential images, to identify blood vessels with active blood flow. The requirement for multi-scan-volumetric OCT can reduce OCTA imaging speed, which will induce eye movements, and limit the image field-of-view. In principle, the blood flow should also affect the spatially vascular connectivity (SVC), i.e., reflectance brightness profile along the vessel direction, in a single-scan-volumetric OCT. In other words, the SVC in single-scan-volumetric OCT might be equivalent to the TVC in multi-scan-volumetric OCT for high fidelity OCTA construction. In this article, we report an SVC network (SVC-Net) for deep learning OCTA construction from single-scan-volumetric OCT. The effect of SVC for deep learning OCTA was evaluated by SVC-based speckle variance calculation, revealing three adjacent B-scans has the optimal performance. We further compare the effects of SVC with three adjacent B-scans and a single B-scan inputs into SVC-Net. The structural-similarity index measure (SSIM) loss function was selected to optimize deep learning contrast enhancement of microstructures, such as microcapillaries, in OCT. This was verified by comparative analysis of the SVC-Net performances with SSIM and mean-squared-error (MSE) loss functions. The combination of SVC involvement and SSIM loss function enabled microcapillary resolution OCTA construction from single-scan-volumetric OCT. The performance of the SVC-Net was verified with OCT datasets from both superficial and deep vascular plexus in mouse and human eyes.

Differential Artery–Vein Analysis Improves OCTA Classification of Diabetic Retinopathy

“…Extensive research has focused on quantitative analysis of OCTA images for the objective detection and classification of retinal diseases [12][13][14][15][16][17][18][19][20][21][22][23]. Previous work by Alam et al [24] and Son et al [25] leveraged en face OCT and en face OCTA features for AV segmentation through quantitative feature analysis.…”

Differential Artery–Vein Analysis Improves OCTA Classification of Diabetic Retinopathy

“…Extensive research has focused on quantitative analysis of OCTA images for the objective detection and classification of retinal diseases [12][13][14][15][16][17][18][19][20][21][22][23]. Previous work by Alam et al [24] and Son et al [25] leveraged en face OCT and en face OCTA features for AV segmentation through quantitative feature analysis.…”

Optimizing OCTA layer fusion option for deep learning classification of diabetic retinopathy

“…However, it requires dye injections which can cause adverse side effects. In contrast, optical coherence tomography (OCT) angiography (OCTA) is a non-invasive imaging method that provides volumetric data of the retinal and choroidal layers [16,17], and has been demonstrated to be more sensitive than FA in detecting subtle abnormalities [18].…”