Deep learning enables extraction of capillary-level angiograms from single OCT volume

TL;DR

This paper introduces a deep learning method that extracts detailed capillary-level angiograms from a single OCT scan, significantly reducing data acquisition time in ophthalmology imaging.

Contribution

A novel deep learning algorithm combining fovea attention and residual networks enables capillary-level angiogram extraction from single OCT volumes, improving visualization accuracy.

Findings

Better visualization of capillaries around the foveal avascular zone.

Outperforms existing U-Net based methods in capillary extraction.

Reduces the need for multiple OCT scans.

Abstract

Optical coherence tomography angiography (OCTA) has drawn numerous attentions in ophthalmology. However, its data acquisition is time-consuming, because it is based on temporal-decorrelation principle thus requires multiple repeated volumetric OCT scans. In this paper, we developed a deep learning algorithm by combining a fovea attention mechanism with a residual neural network, which is able to extract capillary-level angiograms directly from a single OCT scan. The segmentation results of the inner limiting membrane and outer plexiform layers and the central $1\times1$ mm$^2$ field of view of the fovea are employed in the fovea attention mechanism. So the influences of large retinal vessels and choroidal vasculature on the extraction of capillaries can be minimized during the training of the network. The results demonstrate that the proposed algorithm has the capacity to better-visualizing capillaries around the foveal avascular zone than the existing work using a U-Net architecture.