Advertisement

Fully Automatic Segmentation of Coronary Arteries Based on Deep Neural Network in Intravascular Ultrasound Images

  • Sekeun Kim
  • Yeonggul Jang
  • Byunghwan Jeon
  • Youngtaek Hong
  • Hackjoon ShimEmail author
  • Hyukjae Chang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11043)

Abstract

Accurate segmentation of coronary arteries is important for the diagnosis of cardiovascular diseases. In this paper, we propose a fully convolutional neural network to efficiently delineate the boundaries of the wall and lumen of the coronary arteries using intravascular ultrasound (IVUS) images. Our network addresses multi-label segmentation of the wall and lumen areas at the same time. The primary body of the proposed network is U-shaped which contains the encoding and decoding paths to learn rich hierarchical representations. The multi-scale input layer is adapted to take a multi-scale input. We deploy a multi-label loss function with weighted pixel-wise cross-entropy to alleviate imbalance of the rate of background, wall, and lumen. The proposed method is compared with three existing methods and the segmentation results are measured on four metrics, dice similarity coefficient, Jaccard index, percentage of area difference, and Hausdorff distance on totally 38,478 IVUS images from 35 subjects.

Keywords

Intravascular ultrasound (IVUS) Machine learning Image segmentation Computer-aided diagnosis 

References

  1. 1.
    Balocco, S., et al.: Standardized evaluation methodology and reference database for evaluating IVUS image segmentation. Comput. Med. Imaging Graph. 38(2), 70–90 (2014)CrossRefGoogle Scholar
  2. 2.
    Downe, R., et al.: Segmentation of intravascular ultrasound images using graph search and a novel cost function. In: Proceedings of the 2nd MICCAI Workshop on Computer Vision for Intravascular and Intracardiac Imaging, pp. 71–79. Citeseer (2008)Google Scholar
  3. 3.
    Faraji, M., Cheng, I., Naudin, I., Basu, A.: Segmentation of arterial walls in intravascular ultrasound cross-sectional images using extremal region selection. Ultrasonics 84, 356–365 (2018)CrossRefGoogle Scholar
  4. 4.
    Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)Google Scholar
  5. 5.
    Hattori, K., et al.: Impact of statin therapy on plaque characteristics as assessed by serial OCT, grayscale and integrated backscatterivus. JACC Cardiovasc. Imaging 5(2), 169–177 (2012)CrossRefGoogle Scholar
  6. 6.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015)Google Scholar
  7. 7.
    Jeelani, H., Martin, J., Vasquez, F., Salerno, M., Weller, D.S.: Image quality affects deep learning reconstruction of MRI. In: 2018 IEEE 15th International Symposium on Biomedical Imaging, pp. 357–360 (2018)Google Scholar
  8. 8.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2014)Google Scholar
  9. 9.
    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
  10. 10.
    Maturana, D., Scherer, S.: Voxnet: a 3D convolutional neural network for real-time object recognition. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 922–928 (2015)Google Scholar
  11. 11.
    Mehta, R., Sivaswamy, J.: M-net: a convolutional neural network for deep brain structure segmentation. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 437–440 (2017)Google Scholar
  12. 12.
    Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)Google Scholar
  13. 13.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  14. 14.
    Rotger, D., Radeva, P., Fernández-Nofrerías, E., Mauri, J.: Blood detection in IVUS images for 3D volume of lumen changes measurement due to different drugs administration. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds.) CAIP 2007. LNCS, vol. 4673, pp. 285–292. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-74272-2_36CrossRefzbMATHGoogle Scholar
  15. 15.
    Sheet-Populations, S.F.: International cardiovascular disease statistics. American Heart Association (2004)Google Scholar
  16. 16.
    Sonka, M., et al.: Segmentation of intravascular ultrasound images: a knowledge-based approach. IEEE Trans. Med. Imaging 14(4), 719–732 (1995)CrossRefGoogle Scholar
  17. 17.
    Yang, J., Tong, L., Faraji, M., Basu, A.: IVUS-Net: an intravascular ultrasound segmentation network. In: International Conference of Smart Multimedia (2018)Google Scholar
  18. 18.
    Zhang, X., McKay, C.R., Sonka, M.: Tissue characterization in intravascular ultrasound images. IEEE Trans. Med. Imaging 17(6), 889–899 (1998)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sekeun Kim
    • 1
  • Yeonggul Jang
    • 2
  • Byunghwan Jeon
    • 2
  • Youngtaek Hong
    • 2
  • Hackjoon Shim
    • 3
    Email author
  • Hyukjae Chang
    • 4
  1. 1.Graduate Program in Biomedical Engineering the Graduate SchoolYonsei UniversitySeoulSouth Korea
  2. 2.Brain Korea 21 PLUS Project for Medical ScienceYonsei UniversitySeoulSouth Korea
  3. 3.Yonsei-Cedars-Sinai Integrative Cardiac Imaging Research CenterSeoulRepublic of Korea
  4. 4.Division of Cardiology, Severance Cardiovascular HospitalYonsei University College of MedicineSeoulSouth Korea

Personalised recommendations