VascuSynth: simulating vascular trees for generating volumetric image data with ground-truth segmentation and tree analysis

Abstract

Automated segmentation and analysis of tree-like structures from 3D medical images are important for many medical applications, such as those dealing with blood vasculature or lung airways. However, there is an absence of large databases of expert segmentations and analyses of such 3D medical images, which impedes the validation and training of proposed image analysis algorithms. In this work, we simulate volumetric images of vascular trees and generate the corresponding ground-truth segmentations, bifurcation locations, branch properties, and tree hierarchy. The tree generation is performed by iteratively growing a vascular structure based on a user-defined (possibly spatially varying) oxygen demand map. We describe the details of the algorithm and provide a variety of example results.