Three-Dimensional Reconstruction of Intravascular Ultrasound Images Based on Deep Learning

  • Yankun Cao
  • Zhi LiuEmail author
  • Xiaoyan Xiao
  • Yushuo Zheng
  • Lizhen Cui
  • Yixian Du
  • Pengfei Zhang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1181)


Coronary artery disease (CAD), CAD is a common atherosclerotic disease and one of the leading diseases that endanger human health. Acute cardiovascular events are catastrophic, the main cause of which is atherosclerosis (AS) plaque rupture and secondary thrombosis. In order to measure the important parameters such as the diameter, cross-sectional area, volume, wall thickness of the vessel and the size of the AS plaque, it is necessary to first extract the inner and outer membrane edges of the vessel wall in each frame intravascular ultrasound (IVUS) and the plaque edges that may exist. IVUS-based three-dimensional intravascular reconstruction can accurately assess and diagnose the tissue characterization of various cardiovascular diseases to obtain the best treatment options. However, due to the presence of vascular bifurcation in the blood vessels, the presence of bifurcated blood vessels creates great difficulties for the segmentation and reconstruction of the inner and outer membranes. In order to solve this problem, this paper is based on the deep learning method, which first classifies the intravascular bifurcation vessels and normal blood vessels, and then segmentation of the inner and outer membrane, separately. Finally, the three-dimensional reconstruction of the segmented blood vessels is of great significance for the auxiliary diagnosis and treatment of coronary heart disease.


Deep learning Atherosclerosis Intravascular ultrasound images Image classification Three-dimensional reconstruction 


  1. 1.
    Gao, Z., et al.: Robust estimation of carotid artery wall motion using the elasticity-based state-space approach. Med. Image Anal. 37, 1–21 (2017)CrossRefGoogle Scholar
  2. 2.
    Okada, K., Fitzgerald, P.J., Honda, Y.: Intravascular ultrasound. In: Lanzer, P. (ed.) Textbook of Catheter-Based Cardiovascular Interventions, pp. 329–363. Springer, Cham (2018). Scholar
  3. 3.
    Han, G., et al.: Hybrid resampling and multi-feature fusion for automatic recognition of cavity imaging sign in lung CT. Future Gener. Comput. Syst. 99, 558–570 (2019)CrossRefGoogle Scholar
  4. 4.
    Xu, C., et al.: Direct delineation of myocardial infarction without contrast agents using a joint motion feature learning architecture. Med. Image Anal. 50, 82–94 (2018)CrossRefGoogle Scholar
  5. 5.
    Dhungel, N., Carneiro, G., Bradley, A.P.: The automated learning of deep features for breast mass classification from mammograms. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 106–114. Springer, Cham (2016). Scholar
  6. 6.
    Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)CrossRefGoogle Scholar
  7. 7.
    Miao, S., Wang, Z.J., Liao, R.: A CNN regression approach for real-time 2D/3D registration. IEEE Trans. Med. Imaging 35(5), 1352–1363 (2016)CrossRefGoogle Scholar
  8. 8.
    Olszewski, M.E., Wahle, A., Mitchell, S.C., Sonka, M.: Segmentation of intravascular ultrasound images: a machine learning approach mimicking human vision. In: International Congress Series, vol. 1268, pp. 1045–1049. Elsevier (2004)Google Scholar
  9. 9.
    Giannoglou, G.D., et al.: A novel active contour model for fully automated segmentation of intravascular ultrasound images: in vivo validation in human coronary arteries. Comput. Biol. Med. 37(9), 1292–1302 (2007)CrossRefGoogle Scholar
  10. 10.
    Mendizabal-Ruiz, G., Rivera, M., Kakadiaris, I.A.: A probabilistic segmentation method for the identification of luminal borders in intravascular ultrasound images. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)Google Scholar
  11. 11.
    Zhu, X., Zhang, P., Shao, J., Cheng, Y., Zhang, Y., Bai, J.: A snake-based method for segmentation of intravascular ultrasound images and its in vivo validation. Ultrasonics 51(2), 181–189 (2011)CrossRefGoogle Scholar
  12. 12.
    Dehnavi, S.M., Babu, M.P., Yazchi, M., Basij, M.: Automatic soft and hard plaque detection in IVUS images: a textural approach. In: 2013 IEEE Conference on Information and Communication Technologies, pp. 214–219. IEEE (2013)Google Scholar
  13. 13.
    Gao, Z., et al.: Automated framework for detecting lumen and mediacadventitia borders in intravascular ultrasound images. Ultrasound Med. Biol. 41(7), 2001–2021 (2015)CrossRefGoogle Scholar
  14. 14.
    Su, S., Hu, Z., Lin, Q., Hau, W.K., Gao, Z., Zhang, H.: An artificial neural network method for lumen and media-adventitia border detection in ivus. Comput. Med. Imaging Graph. 57, 29–39 (2017)CrossRefGoogle Scholar
  15. 15.
    Yang, J., Tong, L., Faraji, M., Basu, A.: IVUS-Net: an intravascular ultrasound segmentation network. In: Basu, A., Berretti, S. (eds.) ICSM 2018. LNCS, vol. 11010, pp. 367–377. Springer, Cham (2018). Scholar
  16. 16.
    Bouvrie, J.: Notes on convolutional neural networks (2006)Google Scholar
  17. 17.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRefGoogle Scholar
  18. 18.
    LeCun, Y., et al.: Learning algorithms for classification: a comparison on handwritten digit recognition. Neural Netw.: Stat. Mech. Perspect. 261, 276 (1995)Google Scholar
  19. 19.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  20. 20.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  21. 21.
    Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelME: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77(1–3), 157–173 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Yankun Cao
    • 1
  • Zhi Liu
    • 1
    Email author
  • Xiaoyan Xiao
    • 2
  • Yushuo Zheng
    • 3
  • Lizhen Cui
    • 4
  • Yixian Du
    • 5
  • Pengfei Zhang
    • 2
  1. 1.Intelligent Medical Information Processing, School of Information Science and EngineeringShandong UniversityQingdaoChina
  2. 2.Qilu HospitalShandong UniversityJinanChina
  3. 3.High School Attached to Shandong Normal UniversityJinanChina
  4. 4.School of SoftwareShandong UniversityJinanChina
  5. 5.School of Computer ScienceFudan UniversityShanghaiChina

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