  Optical Coherence Tomography Angiography (OCTA) has been increasingly used in
the management of eye and systemic diseases in recent years. Manual or
automatic analysis of blood vessel in 2D OCTA images (en face angiograms) is
commonly used in clinical practice, however it may lose rich 3D spatial
distribution information of blood vessels or capillaries that are useful for
clinical decision-making. In this paper, we introduce a novel 3D vessel
reconstruction framework based on the estimation of vessel depth maps from OCTA
images. First, we design a network with structural constraints to predict the
depth of blood vessels in OCTA images. In order to promote the accuracy of the
predicted depth map at both the overall structure- and pixel- level, we combine
MSE and SSIM loss as the training loss function. Finally, the 3D vessel
reconstruction is achieved by utilizing the estimated depth map and 2D vessel
segmentation results. Experimental results demonstrate that our method is
effective in the depth prediction and 3D vessel reconstruction for OCTA
images.% results may be used to guide subsequent vascular analysis
