3D Vessel Reconstruction in OCT-Angiography via Depth Map Estimation

TL;DR

This paper presents a novel deep learning framework for reconstructing 3D blood vessel structures from OCT-Angiography images by estimating vessel depth maps, enhancing clinical analysis capabilities.

Contribution

It introduces a new network with structural constraints for accurate vessel depth prediction from OCTA images, improving 3D vessel reconstruction accuracy.

Findings

Effective depth prediction demonstrated in experiments

Improved 3D vessel reconstruction accuracy

Potential to enhance clinical vascular analysis

Abstract

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