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In this paper, we propose an approach to accurately modelling tubular, anatomical structures as curvilinear entities. Current optimal path and centerline extraction techniques are either prone to introducing spurious tortuosity, or unable to consistently avoid taking shortcuts at high curvature positions. These problems not only affect spatial appreciation of the structure but may also significantly impact the accuracy of length, angle and tortuosity measurements. Our approach overcomes the above deficiencies through the combination of a front propagation method and a model in which a priori shape knowledge is embedded. This approach is designed to be used in endovascular and neurological surgical planning. The efficacy of our method is demonstrated using synthetic and clinical data.

Combining Front Propagation with Shape Knowledge for Accurate Curvilinear Modelling