Optical coherence tomography angiography (OCTA) is a pivotal imaging modality for non-invasive visualization of the retinal microvasculature, but current clinical OCTA systems lack the capability to segment and quantify vascular features separately for arteries and veins. This study introduces OCTA-ReVA+AV, an open-source, fully automated toolbox that integrates deep learning-based artery–vein (AV) segmentation and vessel-specific quantitative analysis of OCTA images. OCTA-ReVA+AV computes a comprehensive set of vascular metrics including, blood vessel density (BVD), vessel skeleton density (VSD), vessel perimeter index (VPI), blood vessel caliber (BVC), blood vessel tortuosity (BVT), vessel complexity index (VCI), perfusion intensity density (PID), vessel area flux (VAF), and normalized blood flow index (NBFI) independently for arteries and veins. These features are extracted within a user-friendly graphical interface and demonstrate high repeatability and segmentation consistency. By separately quantifying arterial and venous alterations, OCTA-ReVA+AV fills a critical gap in OCTA analytics, enhancing detection and monitoring of retinal vascular diseases.
Yao et al. (Mon,) studied this question.