3D Retinal Vessel Tree Segmentation and Reconstruction with OCT Images

Detection and analysis of the arterio-venular tree of the retina is a relevant issue, providing useful information in procedures such as the diagnosis of different pathologies. Classical approaches for vessel extraction make use of 2D acquisition paradigms and, therefore, obtain a limited representation of the vascular structure. This paper proposes a new methodology for the automatic 3D segmentation and reconstruction of the retinal arterio-venular tree in Optical Coherence Tomography (OCT) images. The methodology takes advantage of different image analysis techniques to initially segment the vessel tree and estimate its calibers along it. Then, the corresponding depth for the entire vessel tree is obtained. Finally, with all this information, the method performs the 3D reconstruction of the entire vessel tree. The test and validation procedure employed 196 OCT histological images with the corresponding near infrared reflectance retinographies. The methodology showed promising results, demonstrating its accuracy in a complex domain, providing a coherent 3D vessel tree reconstruction that can be posteriorly analyzed in different medical diagnostic processes.