3D Microvascular Reconstruction in Retinal OCT Angiography Images via Domain-Adaptive Learning

Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique
that enables the acquisition of 3D depth-resolved information with micrometer
resolution, facilitating the diagnosis of various eye-related diseases. In OCTA-based
image analysis, 2D en face projected images are commonly used for quantifying microvascular
changes, while the 3D images with rich depth information remains largely
unexplored. This is mainly due to that direct 3D vessel reconstruction faces several
challenges, including projection artifacts, complex vessel topology, and high computational
cost. These limitations hinder comprehensive microvascular analysis and
may obscure potentially vital 3D vessel biomarkers. In this study, we propose a novel
method for 3D reconstruction of retinal microvasculature using 2D en face images.
Our approach capitalizes on a elaborately generated 2D OCTA depth map for vessel
reconstruction, thus eliminating the need for unavailable 3D volumetric data in certain
retinal imaging devices. More specifically, we first build a structure-guided depth prediction
network which incorporates a domain adaptation module to evaluate the depth
maps obtained from different OCTA imaging devices. A point-cloud-to-surface reconstruction
method is then utilized to reconstruct the corresponding 3D retinal vessels,
based on the predicted depth maps and 2D vascular information. Experimental results
demonstrate the superior performance of our method in comparison to existing stateof-
the-art techniques. Furthermore, we extract 3D vessel-related features to assess disease
correlation and classification, effectively evaluating the potential of our method
for guiding subsequent clinical analysis. The results show promise of exploring 3D
microvascular analysis for early diagnosis of various eye-related diseases.