Inference of Cerebrovascular Topology with Geodesic Minimum Spanning Trees

Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jager, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

A vectorial representation of the vascular network that embodies quantitative features – location, direction, scale, bifurcations – has many potential cardio- and neuro-vascular applications. We present VTrails, an end-to-end approach to extract geodesic vascular minimum spanning trees from angiographic data by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Evaluating real and synthetic vascular images, we compare VTrails against the state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field. The inferred geodesic trees are then quantitatively evaluated within a topologically-aware framework, by comparing the proposed method against popular vascular segmentation tool-kits on clinical angiographies. VTrails potentials are discussed towards integrating group-wise vascular image analyses. The performance of VTrails demonstrates its versatility and usefulness also for patient-specific applications in interventional neuroradiology and vascular surgery.

Original languageEnglish
JournalIEEE Transactions on Medical Imaging
DOIs
Publication statusAccepted/In press - 26 Jul 2018

Keywords

  • Blood vessels
  • brain
  • connectivity
  • Feature extraction
  • Image segmentation
  • Imaging
  • Kernel
  • Network topology
  • Three-dimensional displays
  • Topology
  • vascular tree

Fingerprint

Dive into the research topics of 'Inference of Cerebrovascular Topology with Geodesic Minimum Spanning Trees'. Together they form a unique fingerprint.

Cite this