Robust Optimization-Based Coronary Artery Labeling From X-Ray Angiograms

In this paper, we present an efficient robust labeling method for coronary arteries from X-ray angiograms based on energy optimization. The fundamental goal of this research is to facilitate the analysis and diagnosis of interventional surgery in the most efficient way, and such effort could also improve the performance during doctor training, and surgery simulation and planning. Compared to the prior state-of-the-art, our method is much more robust to resist noises and is tolerant to even incomplete data because of the "built-in" nature of global optimization. We start with a fully parallelized algorithm based on Hessian matrix to extract the tubular structure from the X-ray angiograms as vessel candidates. Then, instead of using the candidates directly, we use the grow cut (Vezhnevets and V. Konouchine, Growcut: Interactive multi-label N-D image segmentation by cellular automata, in Proc. of Graphicon, 2005, pp. 150-156.) method, which is similar to graph cut (Boykov et al. , Fast approximate energy minimization via graph cuts, IEEE Trans. Pattern Anal. Mach. Intell. , vol. 23, no. 11, pp. 1222-1239, Nov. 2001.)but with better performance to extract the precise vessel structure from the images. Next, we use the fast marching method with second derivatives and cross neighbors to extract the accurate skeleton segments. After that, we propose an efficient method based on iterative closest point (Z. Zhang, Iterative point matching for registration of free-form curves and surfaces, Int J. Comput. Vis., vol. 13, no. 2, pp. 119-152, 1994.) to organize the skeleton segments by treating the continuity and similarity as extra constraints. Finally, we formulate the vessel labeling problem as an energy optimization problem and solve it using belief propagation. We also demonstrate several typical applications including flow velocity estimation, heart beat estimation, and vessel diameter estimation to show its practical uses in clinical diagnosis and treatment. Our experiments exhibit the correctness and robustness, as well as the high performance of our algorithm. We envision that our system would be of high utility for diagnosis and therapy to treat vessel-related diseases in a clinical setting in the near future.