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Monte-carlo Sampling, Particle Filters and Segmentation of Coronaries

In this report, we propose a particle filter-based propagation approach for the segmentation of vascular structures in 3D volumes. In our approach, successive planes of the structure are modeled as states of a particle filter. Such a state consists in the 3D structure, orientation, position, geometric form and appearance in statistical means. In order to account for bifurcations and branchings, we consider a Monte Carlo sampling rule that propagates in parallel multiple hypotheses. In order to account for prior knowledge, notions of a linear (Kalman) filter are incorporated within the proposed approach. The prior knowledge constrains the vessel detection, which combines edge-driven and region-based metrics. Promising results on the segmentation of coronary arteries demonstrate the potential of the proposed approach.