  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.
