Greedy Soft Matching for Vascular Tracking of Coronary Angiographic Image Sequences

Abstract

Vascular tracking of coronary angiographic image sequences is one of the most clinically important tasks in the diagnostic assessment and interventional guidance of cardiac disease. It is difficult to automate this application because the vascular structure is complex; moreover, unsatisfactory angiography image quality may exacerbate the difficulty of vasculature extraction. This paper converts vascular tracking into branch matching and proposes a novel and automatic greedy soft match algorithm. Our method is based on a graph framework. A graph model building module is proposed to represent the vascular structure. Then, a greedy branch searching method is adopted to acquire all possible paths in the graph that may match the reference vessel. Finally, a soft batch matching method that combines branch descriptor and dynamic time warping is presented to select the best matching branch. The solution to the problem takes advantage of both spatial and temporal continuity between successive frames. The experimental results demonstrate that the proposed algorithm is effective and robust for vascular tracking. The F1 score of a single branch dataset, which contains 12 angiographic image sequences with 77 angiograms of contrast agent-filled vessels, is 0.89 ± 0.06 and of a vessel tree dataset which contains nine sequences with 58 angiograms is 0.88 ± 0.05. Extensive experimental results well demonstrate the superior performance of the algorithm. In addition, it provides a universal solution to address the problem of filamentary structure tracking.