Skip to main content

Efficient 3D Deep Learning for Myocardial Diseases Segmentation

  • Conference paper
  • First Online:
Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges (STACOM 2020)


Automated myocardial segmentation from late gadolinium enhancement magnetic resonance images (LGE-MRI) is a critical step in the diagnosis of cardiac pathologies such as ischemia and myocardial infarction. This paper proposes a deep learning framework for improved myocardial diseases segmentation. In the first step, we build an encoder-decoder segmentation network that generates myocardium and cavity segmentations from the whole volume, followed by a 3D U-Net based on Shape prior to identifying myocardial infarction and myocardium ventricular obstruction (MVO) segmentations from the encoder-decoder prediction. The proposed network achieves good segmentation performance, as computed by average Dice ratio overall predicted substructures, respectively: ’Myocardium’: 96.29%, ’Infarctus’: 76.56%, ’MVO’: 93.12% on our validation EMIDEC dataset consisting of LGE-MRI volumes of 16 patients extracted from the training data.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
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

Similar content being viewed by others


  1. Mackay, J., Mensah, G.A.: The Atlas of Heart Disease and Stroke. World Health Organization (2004)

    Google Scholar 

  2. Surawicz, B., Knilans, T.: Chou’s Electrocardiography in Clinical Practice E-Book: Adult and Pediatric. Elsevier Health Sciences (2008)

    Google Scholar 

  3. Kim, R.J., et al.: Relationship of MRI delayed contrast enhancement to irreversible injury, infarct age, and contractile function. Circulation 100(19), 1992–2002 (1999)

    Article  Google Scholar 

  4. Amado, L.C., et al.: Accurate and objective infarct sizing by contrast-enhanced magnetic resonance imaging in a canine myocardial infarction model. J. Am. Coll. Cardiol. 44(12), 2383–2389 (2004)

    Article  Google Scholar 

  5. Albà, X., Figueras i Ventura, R.M., Lekadir, K., Frangi, A.F.: Healthy and scar myocardial tissue classification in DE-MRI. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2012. LNCS, vol. 7746, pp. 62–70. Springer, Heidelberg (2013).

    Chapter  Google Scholar 

  6. Carminati, M.C., et al.: Comparison of image processing techniques for nonviable tissue quantification in late gadolinium enhancement cardiac magnetic resonance images. J. Thorac. Imaging 31(3), 168–176 (2016)

    Article  Google Scholar 

  7. 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).

    Chapter  Google Scholar 

  8. Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multistructures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514–2525 (2018)

    Article  Google Scholar 

  9. Fahmy, A.S., et al.: Automated cardiac MR scar quantification in hypertrophic cardiomyopathy using deep convolutional neural networks. JACC: Cardiovasc. Imaging 11(12), 1917–1918 (2018)

    Google Scholar 

  10. Zabihollahy, F., White, J.A., Ukwatta, E.: Fully automated segmentation of left ventricular myocardium from 3D late gadolinium enhancement magnetic resonance images using a U-net convolutional neural network-based model. In: Medical Imaging 2019: Computer-Aided Diagnosis. International Society for Optics and Photonics, vol. 10950, p. 109503C, March 2019

    Google Scholar 

  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. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016).

    Chapter  Google Scholar 

  12. Dou, Q., et al.: 3D deeply supervised network for automated segmentation of volumetric medical images. Med. Image Anal. 41, 40–54 (2017)

    Article  Google Scholar 

  13. Yu, L., Yang, X., Qin, J., Heng, P.-A.: 3D FractalNet: dense volumetric segmentation for cardiovascular MRI volumes. In: Zuluaga, M.A., Bhatia, K., Kainz, B., Moghari, M.H., Pace, D.F. (eds.) RAMBO/HVSMR -2016. LNCS, vol. 10129, pp. 103–110. Springer, Cham (2017).

    Chapter  Google Scholar 

  14. Xu, C., et al.: Direct delineation of myocardial infarction without contrast agents using a joint motion feature learning architecture. Med. Image Anal. 50, 82–94 (2018)

    Article  Google Scholar 

  15. Lalande, A., et al.: EMIDEC: a database usable for the automatic evaluation of myocardial infarction from delayed-enhancement cardiac MRI. Data 5, 89 (2020).

    Article  Google Scholar 

  16. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  17. Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801–818 (2018)

    Google Scholar 

  18. Ramachandran, P., Zoph, B., Le, Q. V.: Searching for activation functions. arXiv preprint arXiv:1710.05941 (2017)

  19. Huang, Y., Wang, Q., Jia, W., He, X.: See more than once-kernel-sharing atrous convolution for semantic segmentation. arXiv preprint arXiv:1908.09443 (2019)

Download references

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Khawla Brahim , Abdul Qayyum , Alain Lalande , Arnaud Boucher , Anis Sakly or Fabrice Meriaudeau .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Brahim, K., Qayyum, A., Lalande, A., Boucher, A., Sakly, A., Meriaudeau, F. (2021). Efficient 3D Deep Learning for Myocardial Diseases Segmentation. In: Puyol Anton, E., et al. Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges. STACOM 2020. Lecture Notes in Computer Science(), vol 12592. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68106-7

  • Online ISBN: 978-3-030-68107-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics