Advertisement

Prediction of FFR from IVUS Images Using Machine Learning

  • Geena Kim
  • June-Goo Lee
  • Soo-Jin KangEmail author
  • Paul Ngyuen
  • Do-Yoon Kang
  • Pil Hyung Lee
  • Jung-Min Ahn
  • Duk-Woo Park
  • Seung-Whan Lee
  • Young-Hak Kim
  • Cheol Whan Lee
  • Seong-Wook Park
  • Seung-Jung Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11043)

Abstract

We present a machine learning approach for predicting fractional flow reserve (FFR) from intravscular ultrasound images (IVUS) in coronary arteries. IVUS images and FFR measurements were collected from 1744 patients and 1447 lumen and plaque segmentation masks were generated from 1447 IVUS images using an automatic segmentation model trained on separate 70 IVUS images and minor manual corrections. Using total 114 features from the masks and general patient informarion, we trained random forest (RF), extreme gradient boost (XGBoost) and artificial neural network (ANN) models for a binary classification of FFR-80 threshold (FFR < 0.8 v.s. FFR \(\ge \) 0.8) for comparison. The ensembled XGBoost models evaluated in 290 unseen cases achieved 81% accuracy and 70% recall.

Keywords

Machine learning Fractional flow reserve Intravascular ultrasound Extreme gradient boost Deep neural network Fully convolutional neural network 

Notes

Acknowledgement

This study was supported by grants from the Korea Healthcare Technology R&D Project, Ministry for Health & Welfare Affairs, Republic of Korea (HI15C1790 and HI17C1080); the Ministry of Science and ICT (NRF-2017R1A2B4005886); and the Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea (2017-0745).

References

  1. 1.
    Young, D.F., Cholvin, N.R., Kirkeeide, R.L., Roth, A.C.: Hemodynamics of arterial stenoses at elevated flow rates. Circ. Res. 41, 99–107 (1977)CrossRefGoogle Scholar
  2. 2.
    Pijls, N.H., van Son, J.A., Kirkeeide, R.L., De Bruyne, B., Gould, K.L.: Experimental basis of determining maximum coronary, myocardial, and collateral blood flow by pressure measurements for assessing functional stenosis severity before and after percutaneous transluminal coronary angioplasty. Circulation 87, 1354–1367 (1993)CrossRefGoogle Scholar
  3. 3.
    Pijls, N.H., et al.: Measurement of fractional flow reserve to assess the functional severity of coronary-artery stenoses. N. Engl. J. Med. 334, 1703–1708 (1996)CrossRefGoogle Scholar
  4. 4.
    Tonino, P.A.: Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. N. Engl. J. Med. 360, 213–224 (2009)CrossRefGoogle Scholar
  5. 5.
    De Bruyne, B., Pijls, N.H., Kalesan, B., Barbato, E., Tonino, P.A., Piroth, Z.: Fractional flow reserve-guided PCI versus medical therapy in stable coronary disease. N. Engl. J. Med. 367, 991–1001 (2012)CrossRefGoogle Scholar
  6. 6.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  7. 7.
    Shen, D., Wu, G., Suk, H.-I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221–248 (2017)CrossRefGoogle Scholar
  8. 8.
    Lee, J.-G., et al.: Deep learning in medical imaging: general overview. Korean J. Radiol. 18, 570–584 (2017)CrossRefGoogle Scholar
  9. 9.
    Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 , pp. 785–794 (2016)Google Scholar
  10. 10.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Statist. 29, 1189–1232 (2001)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)CrossRefGoogle Scholar
  12. 12.
    Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  14. 14.
    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

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Geena Kim
    • 1
  • June-Goo Lee
    • 3
  • Soo-Jin Kang
    • 2
    Email author
  • Paul Ngyuen
    • 1
  • Do-Yoon Kang
    • 2
  • Pil Hyung Lee
    • 2
  • Jung-Min Ahn
    • 2
  • Duk-Woo Park
    • 2
  • Seung-Whan Lee
    • 2
  • Young-Hak Kim
    • 2
  • Cheol Whan Lee
    • 2
  • Seong-Wook Park
    • 2
  • Seung-Jung Park
    • 2
  1. 1.College of Computer and Information SciencesRegis UniversityDenverUSA
  2. 2.Department of CardiologyUniversity of Ulsan College of Medicine, Asan Medical CenterSeoulKorea
  3. 3.Biomedical Engineering Research CenterAsan Institute for Life SciencesSeoulKorea

Personalised recommendations