VTrails: Inferring Vessels with Geodesic Connectivity Trees

Abstract

The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative features such as spatial location, direction and scale. In this paper we present an end-to-end approach to extract an acyclic vascular tree from angiographic data by solving a connectivity-enforcing anisotropic fast marching over a voxel-wise tensor field representing the orientation of the underlying vascular tree. The method is validated using synthetic and real vascular images. We compare VTrails against classical and state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field as proof of concept. VTrails performance is evaluated on images with different levels of degradation: we verify that the extracted vascular network is an acyclic graph (i.e. a tree), and we report the extraction accuracy, precision and recall.

References in corpus (1)

• Scale factor point spread function matching: Beyond aliasing in image resampling

Cited by in corpus (4)

• Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction
• Minimal Paths for Tubular Structure Segmentation with Coherence Penalty and Adaptive Anisotropy
• Elastic Registration of Geodesic Vascular Graphs
• Trajectory Grouping with Curvature Regularization for Tubular Structure Tracking