Extraction of coronary vessels in fluoroscopic X-ray sequences using vessel correspondence optimization

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Abstract

We present a method to extract coronary vessels from fluoroscopic x-ray sequences. Given the vessel structure for the source frame,vessel correspondence candidates in the subsequent frame are generated by a novel hierarchical search scheme to overcome the aperture problem. Optimal correspondences are determined within a Markov random field optimization framework. Post-processing is performed to extract vessel branches newly visible due to the inflow of contrast agent. Quantitative and qualitative evaluation conducted on a dataset of 18 sequences demonstrate the effectiveness of the proposed method.

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Shin, S. Y., Lee, S., Noh, K. J., Yun, I. D., & Lee, K. M. (2016). Extraction of coronary vessels in fluoroscopic X-ray sequences using vessel correspondence optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9902 LNCS, pp. 308–316). Springer Verlag. https://doi.org/10.1007/978-3-319-46726-9_36

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