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Inference of Cerebrovascular Topology with Geodesic Minimum Spanning Trees.

Moriconi, S; Zuluaga, MA; Jager, HR; Nachev, P; Ourselin, S; Cardoso, MJ; (2018) Inference of Cerebrovascular Topology with Geodesic Minimum Spanning Trees. IEEE Transactions on Medical Imaging 10.1109/TMI.2018.2860239. (In press). Green open access

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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.

Type: Article
Title: Inference of Cerebrovascular Topology with Geodesic Minimum Spanning Trees.
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TMI.2018.2860239
Publisher version: http://dx.doi.org/10.1109/TMI.2018.2860239
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
Keywords: Topology, Image segmentation, Kernel, Network topology, Three-dimensional displays, Feature extraction, Imaging, Blood vessels, brain, vascular tree, connectivity
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: http://discovery.ucl.ac.uk/id/eprint/10054357
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