Moriconi, S;
Zuluaga, MA;
Jager, HR;
Nachev, P;
Ourselin, S;
Cardoso, MJ;
(2019)
Inference of Cerebrovascular Topology with Geodesic Minimum Spanning Trees.
IEEE Transactions on Medical Imaging
, 38
(1)
pp. 225-239.
10.1109/TMI.2018.2860239.
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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.
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