Model-guided extraction of coronary vessel structures in 2D X-ray angiograms

Review. Automatic Segmentation Techniques of the Coronary Artery Using CT Images in Acute Coronary Syndromes

Coronary artery disease represents one of the leading reasons of death worldwide, and acute coronary syndromes are their most devastating consequences. It is extremely important to identify the patients at risk for developing an acute myocardial infarction, and this goal can be achieved using noninvasive imaging techniques. Coronary computed tomography angiography (CCTA) is currently one of the most reliable methods used for assessing the coronary arteries; however, its use in emergency settings is sometimes limited due to time constraints. This paper presents the main characteristics of plaque vulnerability, the role of CCTA in the assessment of vulnerable plaques, and automatic segmentation techniques of the coronary artery tree based on CT angiography images. A detailed inventory of existing methods is given, representing the state-of-the-art of computational methods applied in vascular system segmentation, focusing on the current applications in acute coronary syndromes.

Vessel tree tracking in angiographic sequences

We present a method to track vessels in angiography [contrast filled vessels in two-dimensional (2-D) x-ray fluoroscopy]. Finding correspondence of a vessel tree from consecutive angiogram frames provides significant value in computer-aided clinical applications such as fast vessel tree segmentation, three-dimensional (3-D) vessel topology reconstruction from corresponding centerlines, cardiac motion understanding, etc. However, establishing an accurate vessel tree correspondence (vessel tree tracking) is a nontrivial problem due to nonlinear periodic cardiac and breathing motion in 2-D views, foreshortening, false bifurcations due to 3-D to 2-D projection, occlusion from other anatomies, etc. The vessel tree is represented by BSpline curves. The control points of the BSpline curves are landmarks that are the tracking targets. Our method maximizes the appearance similarity while preserving the vessel structure. A directed acyclic graph (DAG) is employed to represent the appearance and shape structure of the vessel tree: nodes from the DAG encode the appearance of the vessel tree landmarks, and the edges encode the relative locations between landmarks. The vessel tree tracking problem turns into finding the most similar tree from the DAG in the next frame, and it is solved using an efficient dynamic programming algorithm. We performed evaluations on 62 x-ray angiography sequences (above 1000 frames). Experiment results show our algorithm is robust to these challenges and delivers better performance, compared to four existing methods.