Robust discriminative wire structure modeling with application to stent enhancement in fluoroscopy

Abstract

Learning-based methods have been widely used in detecting landmarks or anatomical structures in various medical imaging applications. The performance of discriminative learning techniques has been demonstrated superior to traditional low-level filtering in robustness and scalability. Nevertheless, some structures and patterns are more difficult to be defined by such methods and complicated and ad-hoc methods still need to be used, e.g. a non-rigid and highly deformable wire structure. In this paper, we propose a novel scheme to train classifiers to detect the markers and guide wire segment anchored by markers. The classifier utilizes the markers as the end point and parameterizes the wire in-between them. The probabilities of the markers and the wire are integrated in a Bayesian framework. As a result, both the marker and the wire detection are improved by such a unified approach. Promising results are demonstrated by quantitative evaluation on 263 fluoroscopic sequences with 12495 frames. Our training scheme can further be generalized to localize longer guidewire with higher degrees of parameterization.