VTrails: Inferring Vessels with Geodesic Connectivity Trees
This work addresses vessel morphology analysis for cardiovascular and neurovascular applications, providing patient-specific quantitative features, but it appears incremental as it builds on existing ridge detection methods.
The paper tackles the problem of extracting vascular trees from angiographic data by introducing VTrails, an end-to-end method that uses connectivity-enforcing anisotropic fast marching on a tensor field. The result is validated on synthetic and real images, showing extraction accuracy, precision, and recall, with verification that the network is an acyclic graph.
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.