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Semi-supervised Assessment of Incomplete LV Coverage in Cardiac MRI Using Generative Adversarial Nets

  • Le ZhangEmail author
  • Ali Gooya
  • Alejandro F. Frangi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10557)

Abstract

Cardiac magnetic resonance (CMR) images play a growing role in diagnostic imaging of cardiovascular diseases. Ensuring full coverage of the Left Ventricle (LV) is a basic criteria of CMR image quality. Complete LV coverage, from base to apex, precedes accurate cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time-consuming and usually done retrospectively in large imaging cohorts. In this paper, we propose a novel semi-supervised method to check the coverage of LV from CMR images by using generative adversarial networks (GAN), we call it Semi-Coupled-GANs (SCGANs). To identify missing basal and apical slices in a CMR volume, a two-stage framework is proposed. First, the SCGANs generate adversarial examples and extract high-level features from the CMR images; then these image attributes are used to detect missing basal and apical slices. We constructed extensive experiments to validate the proposed method on UK Biobank with more than 6000 independent volumetric MR scans, which achieved high accuracy and robust results for missing slice detection, comparable with those of state of the art deep learning methods. The proposed method, in principle, can be adapted to other CMR image data for LV coverage assessment.

References

  1. 1.
    Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint (2016). arXiv:1603.04467
  2. 2.
    Attili, A.K., Schuster, A., Nagel, E., Reiber, J.H., van der Geest, R.J.: Quantification in cardiac MRI: advances in image acquisition and processing. Int. J. Cardiovasc. Imaging 26(1), 27–40 (2010)CrossRefGoogle Scholar
  3. 3.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)Google Scholar
  4. 4.
    Ferreira, P.F., Gatehouse, P.D., Mohiaddin, R.H., Firmin, D.N.: Cardiovascular magnetic resonance artefacts. J. Cardiovasc. Magn. Resonance 15(1), 1 (2013)CrossRefGoogle Scholar
  5. 5.
    Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  6. 6.
    van der Graaf, A., Bhagirath, P., Ghoerbien, S., Götte, M.: Cardiac magnetic resonance imaging: artefacts for clinicians. Neth. Heart J. 22(12), 542–549 (2014)CrossRefGoogle Scholar
  7. 7.
    Klinke, V., Muzzarelli, S., Lauriers, N., Locca, D., Vincenti, G., Monney, P., Lu, C., Nothnagel, D., Pilz, G., Lombardi, M., et al.: Quality assessment of cardiovascular magnetic resonance in the setting of the european CMR registry: description and validation of standardized criteria. J. Cardiovasc. Magn. Resonance 15(1), 1 (2013)CrossRefGoogle Scholar
  8. 8.
    Kumar, N., Berg, A., Belhumeur, P.N., Nayar, S.: Describable visual attributes for face verification and image search. IEEE Trans. Pattern Anal. Mach. Intell. 33(10), 1962–1977 (2011)CrossRefGoogle Scholar
  9. 9.
    Liu, M.Y., Tuzel, O.: Coupled generative adversarial networks. In: Advances in Neural Information Processing Systems, pp. 469–477 (2016)Google Scholar
  10. 10.
    Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of ICML, vol. 30 (2013)Google Scholar
  11. 11.
    Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier gans. arXiv preprint (2016). arXiv:1610.09585
  12. 12.
    Pusey, E., Lufkin, R.B., Brown, R., Solomon, M.A., Stark, D.D., Tarr, R., Hanafee, W.: Magnetic resonance imaging artifacts: mechanism and clinical significance. Radiographics 6(5), 891–911 (1986)CrossRefGoogle Scholar
  13. 13.
    Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint (2013). arXiv:1312.6199
  14. 14.
    Zhang, L., Gooya, A., Dong, B., Hua, R., Petersen, S.E., Medrano-Gracia, P., Frangi, A.F.: Automated quality assessment of cardiac MR images using convolutional neural networks. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2016. LNCS, vol. 9968, pp. 138–145. Springer, Cham (2016). doi: 10.1007/978-3-319-46630-9_14 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electronic and Electrical Engineering, Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)University of SheffieldSheffieldUK

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