Adaptive classifier allows enhanced flow contrast in OCT angiography using a histogram-based motion threshold and 3D Hessian analysis-based shape filtering

In this Letter, we propose an adaptive digital classifier for flow contrast enhancement in optical coherence tomography angiography (OCTA). To solve the depth dependence in the initial motion-based classification, a depth-adaptive motion threshold was determined by performing a histogram analysis of an en-face image at each depth and identifying the static and dynamic voxel populations through fitting. In the follow-up shape-based classification, to adapt to the deformed vessel shapes in OCTA, a modified vesselness function along with an anisotropic Gaussian probe kernel was defined, and then a three-dimensional (3D) Hessian analysis-based shape filtering was utilized for effectively removing the residual static voxels. The experimental outcomes validated that the proposed adaptive digital classifier enabled a superior flow contrast by combining both the motion and 3D shape information.