Data-Driven Synthetic Cerebrovascular Models For Validation Of Segmentation Algorithms.

We introduce a novel method to generate biologically grounded synthetic cerebrovasculature models in a datadriven fashion. First, the centerlines of vascular filaments embedded in an acquired imaging volume are obtained by a segmentation algorithm. That imaging volume is reconstructed from a graph encoding of the centerline (i.e., generating the model's ground truth) and the segmentation algorithm is applied to the resultant volume. As the location and characteristics of the vasculature embedded in this volume are known,theaccuracyofthesegmentationalgorithmcanbeassessed. Moreover, because the synthetic volume was reconstructed directly from biological data, an assessment is made on embedded filaments that are representative of the topologicalandgeometricalcharacteristicsofthedataset. Webelieve thatsuchmodels will provide the means necessary for the enhanced evaluation of vascular segmentation algorithms.