Skip to main content
SpringerLink
Log in

Ejection Fraction Estimation Using a Wide Convolutional Neural Network

  • Conference paper
  • First Online:
Image Analysis and Recognition (ICIAR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10317))

Included in the following conference series:

Abstract

We introduce a method that can be used to estimate the ejection fraction and volume of the left ventricle. The method relies on a deep and wide convolutional neural network to localize the left ventricle from MRI images. Then, the systole and diastole images can be determined based on the size of the localized left ventricle. Next, the network is used in order to segment the region of interest from the diastole and systole images. The end systolic and diastolic volumes are computed and then used in order to compute the ejection fraction. By using a localization network before segmentation, we are able to achieve results that are on par with the state-of-the-art and by annotating only 25 training subjects (5% of the available training subjects).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)

    Google Scholar 

  2. Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2147–2154 (2014)

    Google Scholar 

  3. Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)

    Article  MATH  Google Scholar 

  4. Germano, G., Kiat, H., Kavanagh, P.B., Moriel, M., Mazzanti, M., Su, H.T., Train, K.F.V., Berman, D.S.: Automatic quantification of ejection fraction from gated myocardial perfusion spect. J. Nucl. Med. 36(11), 2138 (1995)

    Google Scholar 

  5. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)

    Google Scholar 

  6. Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)

  7. Kabani, A.W., El-Sakka, M.R.: Estimating ejection fraction and left ventricle volume using deep convolutional networks. In: Campilho, A., Karray, F. (eds.) ICIAR 2016. LNCS, vol. 9730, pp. 678–686. Springer, Cham (2016). doi:10.1007/978-3-319-41501-7_76

    Chapter  Google Scholar 

  8. Kaggle: Data science bowl cardiac challenge data. https://www.kaggle.com/c/second-annual-data-science-bowl. Accessed 19 Mar 2016

  9. Kaus, M.R., von Berg, J., Weese, J., Niessen, W., Pekar, V.: Automated segmentation of the left ventricle in cardiac MRI. Med. Image Anal. 8(3), 245–254 (2004)

    Article  Google Scholar 

  10. Korshunova, I.: Diagnosing heart diseases with deep neural networks. http://irakorshunova.github.io/2016/03/15/heart.html. Accessed 01 Feb 2017

  11. 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 

  12. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  13. Lee, T., Liu, Q.: Solution to win the second annual data science bowl. https://github.com/woshialex/diagnose-heart. Accessed 01 Feb 2017

  14. Lin, X., Cowan, B.R., Young, A.A.: Automated detection of left ventricle in 4D MR images: experience from a large study. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 728–735. Springer, Heidelberg (2006). doi:10.1007/11866565_89

    Chapter  Google Scholar 

  15. Lynch, M., Ghita, O., Whelan, P.F.: Automatic segmentation of the left ventricle cavity and myocardium in MRI data. Comput. Biol. Med. 36(4), 389–407 (2006)

    Article  Google Scholar 

  16. Mulholland, J.: Leading and winning team submissions analysis. http://www.datasciencebowl.com/leading-and-winning-team-submissions-analysis/. Accessed 04 Aug 2016

  17. 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 

  18. Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11(285–296), 23–27 (1975)

    Google Scholar 

  19. 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. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). doi:10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  20. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  21. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: OverFeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013)

  22. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  23. Song, H.O., Girshick, R., Jegelka, S., Mairal, J., Harchaoui, Z., Darrell, T.: On learning to localize objects with minimal supervision. arXiv preprint arXiv:1403.1024 (2014)

  24. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  25. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. arXiv preprint arXiv:1409.4842 (2014)

  26. Szegedy, C., Toshev, A., Erhan, D.: Deep neural networks for object detection. In: Advances in Neural Information Processing Systems, pp. 2553–2561 (2013)

    Google Scholar 

  27. de Wit, J.: Third place solution for the second kaggle national datascience bowl. https://github.com/juliandewit/kaggle_ndsb2. Accessed 01 Feb 2017

Download references

Acknowledgements

This research is partially funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). This support is greatly appreciated. We would also like to thank kaggle, Booz Allen Hamilton, and the National Heart, Lung, and Blood Institute (NHLBI) for providing the MRI images.

Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Western Ontario, London, ON, Canada

    AbdulWahab Kabani & Mahmoud R. El-Sakka

Authors
  1. AbdulWahab Kabani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahmoud R. El-Sakka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahmoud R. El-Sakka .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

  3. Politechnique Montreal, Montreal, Québec, Canada

    Farida Cheriet

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Kabani, A., El-Sakka, M.R. (2017). Ejection Fraction Estimation Using a Wide Convolutional Neural Network. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59876-5_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59875-8

  • Online ISBN: 978-3-319-59876-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation