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Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features

  • Fabian Isensee
  • Paul F. Jaeger
  • Peter M. Full
  • Ivo Wolf
  • Sandy Engelhardt
  • Klaus H. Maier-Hein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10663)

Abstract

Cardiac magnetic resonance imaging improves on diagnosis of cardiovascular diseases by providing images at high spatiotemporal resolution. Manual evaluation of these time-series, however, is expensive and prone to biased and non-reproducible outcomes. In this paper, we present a method that addresses named limitations by integrating segmentation and disease classification into a fully automatic processing pipeline. We use an ensemble of UNet inspired architectures for segmentation of cardiac structures such as the left and right ventricular cavity (LVC, RVC) and the left ventricular myocardium (LVM) on each time instance of the cardiac cycle. For the classification task, information is extracted from the segmented time-series in form of comprehensive features handcrafted to reflect diagnostic clinical procedures. Based on these features we train an ensemble of heavily regularized multilayer perceptrons (MLP) and a random forest classifier to predict the pathologic target class. We evaluated our method on the ACDC dataset (4 pathology groups, 1 healthy group) and achieve dice scores of 0.945 (LVC), 0.908 (RVC) and 0.905 (LVM) in a cross-validation over the training set (100 cases) and 0.950 (LVC), 0.923 (RVC) and 0.911 (LVM) on the test set (50 cases). We report a classification accuracy of \(94 \%\) on a training set cross-validation and \(92\%\) on the test set. Our results underpin the potential of machine learning methods for accurate, fast and reproducible segmentation and computer-assisted diagnosis (CAD).

Keywords

Automated cardiac diagnosis challenge Cardiac magnetic resonance imaging Disease prediction Deep learning CNN 

Notes

Acknowledgements

The author Sandy Engelhardt was funded by the German Research Foundation (DFG) as part of project B01, SFB/TRR 125 Cognition-Guided Surgery.

References

  1. 1.
    Cohn, J.N., Ferrari, R., Sharpe, N.: Cardiac remodeling-concepts and clinical implications: a consensus paper from an international forum on cardiac remodeling. JACC 35, 569–582 (2000)CrossRefGoogle Scholar
  2. 2.
    Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A., van Ginneken, B., Sánchez, C.I.: A Survey on Deep Learning in Medical Image Analysis. arXiv preprint arXiv:1702.05747 (2017)
  3. 3.
    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_28 CrossRefGoogle Scholar
  4. 4.
    Zotti, C., Luo, Z., Lalande, A., Humbert, O., Jodoin, P.-M.: Novel Deep Convolution Neural Network Applied to MRI Cardiac Segmentation. arXiv preprint arXiv:1705.08943 (2017)
  5. 5.
    Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, M., Caballero, M., Guerrero, R., Cook, S., de Marvao, A., O’Regan, D., et al.: Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation. arXiv preprint arXiv:1705.08302 (2017)
  6. 6.
    Tran, P.V.: A Fully Convolutional Neural Network for Cardiac Segmentation in Short-axis MRI. arXiv preprint arXiv:1604.00494 (2016)
  7. 7.
    Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. CMIG 31(4), 198–211 (2008)Google Scholar
  8. 8.
    Medrano-Gracia, P., Cowan, B.R., Ambale-Venkatesh, B., Bluemke, D.A., Eng, J., Finn, J.P., Fonseca, C.G., Lima, J.A., Suinesiaputra, A., Young, A.A.: Left ventricular shape variation in asymptomatic populations: the multi-ethnic study of atherosclerosis. JCMR 16(1), 56 (2014)Google Scholar
  9. 9.
    Zhang, X., Ambale-Venkatesh, B., Bluemke, D.A., Cowan, B.R., Finn, J.P., Kadish, A.H., Lee, D.C., Lima, J.A.C., Hundley, W.G., Suinesiaputra, A., Young, A.A., Medrano-Gracia, P.: Information maximizing component analysis of left ventricular remodeling due to myocardial infarction. JTM 13(1), 343 (2015)Google Scholar
  10. 10.
    Automated Cardiac Diagnosis Challenge. https://www.creatis.insa-lyon.fr/Challenge/acdc. Accessed 23 Jun 2017
  11. 11.
    Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016, Part II. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46723-8_49 CrossRefGoogle Scholar
  12. 12.
    Kayalibay, B., Jensen, G., van der Smagt, P.: CNN-Based Segmentation of Medical Imaging Data. arXiv preprint arXiv:1701.03056 (2017)

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Fabian Isensee
    • 1
  • Paul F. Jaeger
    • 1
  • Peter M. Full
    • 2
    • 3
  • Ivo Wolf
    • 3
  • Sandy Engelhardt
    • 2
    • 3
  • Klaus H. Maier-Hein
    • 1
  1. 1.Medical Image ComputingGerman Cancer Research Center (DKFZ)HeidelbergGermany
  2. 2.Division of Computer-assisted Medical InterventionsGerman Cancer Research Center (DKFZ)HeidelbergGermany
  3. 3.Department of Computer ScienceMannheim University of Applied ScienceMannheimGermany

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