Three-dimensional vascular graph construction from depth information of blood vessel centerlines in optical coherence tomography angiography

Abstract

Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique for visualizing high-resolution volumetric vascular networks. Although OCTA has been widely employed in vascular network analysis, most studies have been limited to the analysis of two-dimensional (2D) en-face projection images because leveraging the full potential of OCTA’s three-dimensional (3D) information has been challenging due to projection artifacts beneath blood vessels. In this study, we propose a semi-automatic method for constructing a 3D vascular graph from 3D OCTA images without relying on data-driven learning strategies such as deep learning. The proposed method estimates the depth information of blood vessel centerlines in 2D en-face images and constructs a 3D vascular graph by integrating the depth estimation results for all vessel centerline segments. We demonstrate the effectiveness of the proposed method through experiments conducted on both simulated datasets and real datasets acquired from the dorsal dermis of mice.