A fully-automatic locally adaptive thresholding algorithm for blood vessel segmentation in 3D digital subtraction angiography.

Subarachnoid hemorrhage due to a ruptured cerebral aneurysm is still a devastating disease. Planning of endovascular aneurysm therapy is increasingly based on hemodynamic simulations necessitating reliable vessel segmentation and accurate assessment of vessel diameters. In this work, we propose a fully-automatic, locally adaptive, gradient-based thresholding algorithm. Our approach consists of two steps. First, we estimate the parameters of a global thresholding algorithm using an iterative process. Then, a locally adaptive version of the approach is applied using the estimated parameters. We evaluated both methods on 8 clinical 3D DSA cases. Additionally, we propose a way to select a reference segmentation based on 2D DSA measurements. For large vessels such as the internal carotid artery, our results show very high sensitivity (97.4%), precision (98.7%) and Dice-coefficient (98.0%) with our reference segmentation. Similar results (sensitivity: 95.7%, precision: 88.9% and Dice-coefficient: 90.7%) are achieved for smaller vessels of approximately 1mm diameter.