Background: An automated, unbiased method to accurately label cerebrovascular territories would greatly advance our ability to assess individual stroke patients as well as study large databases. Previous attempts have failed due to the wide variation in normal vascular topography. We test the hypothesis that a nonparametric probabilistic model that learns the configurational characteristics of vascular territories will better annotate the cerebrovasculature.
Methods: In the George Mason Brain Vasculature database, we identified patients with MRA reconstructions segmented into seven major regions (left and right MCA, PCA, and ACA and Circle of Willis). We then augment these labels by manually segmenting the MCA territory into an additional eight regions. We then divide the database into training and validation cohorts to assess the algorithm.
Results: Among 54 patients that met the inclusion criteria, 39 reconstructions were used as training input to the model among the 61 digital reconstructions of human brain arterial structures available. The model was then validated on an independent cohort of 15 patients. The algorithm was found to be 94+-5.2% accurate in annotating the vascular segments.
Conclusions: Kernel density estimation, used in conjunction with a Bayesian inference-based algorithm, can accurately label cerebral vascular territories using spatial and radial features. This process can provide a framework for further vascular segmentation and analysis of artery occlusion in stroke patients.