Accurate Cross-Section Estimation of Blood Vessels in Choroidal Haller's Layer: An Iterative Method based on 3D Tensor Voting.

Various eye diseases, including polypoidal choroidal vasculopathy (PCV) and age-related macular degeneration (AMD), affect choroidal vasculature early, but possibly minutely. However, due to the complex networked structure of the vasculature, it becomes hard to visualize, analyze and detect such changes in 2D OCT B-scan images. In contrast, algorithmic evaluation of cross-section facilitates clinicians in tracing minute variations in the vessel network, and quantifying those correlated with pathologies, potentially leading to early diagnosis. In this context, we proposed a novel method of estimating vessel cross-sections in choroidal Haller's layer. Accuracy of our method was evaluated on synthetic as well as clinical data by trained optometrists, and earned a confidence score of 90%, marking about 60% improvement over estimates based on a well-known tree-based method.