Abstract
Atherosclerotic plaques, the leading cause of heart attack, can be characterized from intravascular optical coherence tomography (IV-OCT) images by doctors. Since lipid accumulation is an important indication of atherosclerotic plaque, we introduced a new convolutional neural network, called Single Shot Plaque Marking Network (SSPM), to develop an automated method that highlights the extent of lipid plaques from IV-OCT images at real-time, which then would help doctors easily find the vulnerable plaque. Compared with previous available methods, our method is capable of marking the suspicious lipid plaque areas in real-time with better time-efficiency and competitive accuracy during the diagnosis. SSPM is tested on IV-OCT human coronary artery imaging dataset, and the result shows that our method is able to mark suspicious lipid-plaque areas at 91 fps on GPU, or 16 fps on CPU, with an accuracy of 87%.
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References
Allender, S., et al.: European cardiovascular disease statistics. Eur. Heart Netw. 3, 11–35 (2008)
Davies, M.J.: Anatomic features in victims of sudden coronary death. Coronary artery pathology. Circulation 85(1 Suppl.), I19–I24 (1992)
Lee, R.T., Libby, P.: The unstable atheroma. Arterioscler. Thromb. Vasc. Biol. 17(10), 1859–1867 (1997)
Virmani, R., Kolodgie, F.D., Burke, A.P., Farb, A., Schwartz, S.M.: Lessons from sudden coronary death. Arterioscler. Thromb. Vasc. Biol. 20(5), 1262–1275 (2000)
Drexler, W., Liu, M., Kumar, A., Kamali, T., Unterhuber, A., Leitgeb, R.A.: Optical coherence tomography today: speed, contrast, and multimodality. J. Biomed. Opt. 19(7), 071412 (2014)
Prakash, A., Hewko, M., Sowa, M., Sherif, S.: Texture based segmentation method to detect atherosclerotic plaque from optical tomography images. In: European Conference on Biomedical Optics, p. 88020S (2013)
Hansson, G.K.: Inflammation, atherosclerosis, and coronary artery disease. N. Engl. J. Med. 352(16), 1685–1695 (2005)
Haralick, R.M., Shanmugam, K., et al.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3(6), 610–621 (1973)
Nam, H.S., et al.: Characterization of lipid-rich plaques using spectroscopic optical coherence tomography. J. Biomed. Opt. 21(7), 075004 (2016)
Jaedicke, V., et al.: Comparison of different metrics for analysis and visualization in spectroscopic optical coherence tomography. Biomed. Opt. Express 4(12), 2945–2961 (2013)
Fleming, C.P., Eckert, J., Halpern, E.F., Gardecki, J.A., Tearney, G.J.: Depth resolved detection of lipid using spectroscopic optical coherence tomography. Biomed. Opt. Express 4(8), 1269–1284 (2013)
Rico-Jimenez, J.J., Campos-Delgado, D.U., Villiger, M., Otsuka, K., Bouma, B.E., Jo, J.A.: Automatic classification of atherosclerotic plaques imaged with intravascular OCT. Biomed. Opt. Express 7(10), 4069–4085 (2016)
Ughi, G.J., Adriaenssens, T., Sinnaeve, P., Desmet, W., D’hooge, J.: Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images. Biomed. Opt. Express 4(7), 1014–1030 (2013)
Athanasiou, L.S., Exarchos, T.P., Naka, K.K., Michalis, L.K., Prati, F., Fotiadis, D.I.: Atherosclerotic plaque characterization in optical coherence tomography images. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011, pp. 4485–4488 (2011)
Gargesha, M., et al.: Parameter estimation of atherosclerotic tissue optical properties from three-dimensional intravascular optical coherence tomography. J. Med. Imaging 2(1), 016001 (2015)
Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Lekadir, K., et al.: A convolutional neural network for automatic characterization of plaque composition in carotid ultrasound. IEEE J. Biomed. Health Inform. 21(1), 48–55 (2017)
Tearney, G.J., et al.: Consensus standards for acquisition, measurement, and reporting of intravascular optical coherence tomography studies. J. Am. Coll. Cardiol. 59(12), 1058–1072 (2012)
Read, P., Meyer, M.-P.: Restoration of Motion Picture Film. Butterworth-Heinemann, Oxford (2000)
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Zhang, C., He, J., Wang, W., Yang, S., Zhang, Y. (2019). A Real-Time Method for Marking the Extent of a Lipid Plaque Based on IV-OCT Imaging. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2018. Communications in Computer and Information Science, vol 1005. Springer, Singapore. https://doi.org/10.1007/978-981-13-7983-3_9
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DOI: https://doi.org/10.1007/978-981-13-7983-3_9
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