Skip to main content


Log in

A digital cardiac disease biomarker from a generative progressive cardiac cine-MRI representation

  • Original Article
  • Published:
Biomedical Engineering Letters Aims and scope Submit manuscript


Cardiac cine-MRI is one of the most important diagnostic tools used to assess the morphology and physiology of the heart during the cardiac cycle. Nonetheless, the analysis on cardiac cine-MRI is poorly exploited and remains highly dependent on the observer's expertise. This work introduces an imaging cardiac disease representation, coded as an embedding vector, that fully exploits hidden mapping between the latent space and a generated cine-MRI data distribution. The resultant representation is progressively learned and conditioned by a set of cardiac conditions. A generative cardiac descriptor is achieved from a progressive generative-adversarial network trained to produce MRI synthetic images, conditioned to several heart conditions. The generator model is then used to recover a digital biomarker, coded as an embedding vector, following a backpropagation scheme. Then, an UMAP strategy is applied to build a topological low dimensional embedding space that discriminates among cardiac pathologies. Evaluation of the approach is carried out by using an embedded representation as a potential disease descriptor in 2296 pathological cine-MRI slices. The proposed strategy yields an average accuracy of 0.8 to discriminate among heart conditions. Furthermore, the low dimensional space shows a remarkable grouping of cardiac classes that may suggest its potential use as a tool to support diagnosis. The learned progressive and generative representation, from cine-MRI slices, allows retrieves and coded complex descriptors that results useful to discriminate among heart conditions. The cardiac disease representation expressed as a hidden embedding vector could potentially be used to support cardiac analysis on cine-MRI sequences.

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

Access this article

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others


  1. Roth GA, Abate D, Abate KH, et al. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1736–88.

    Article  Google Scholar 

  2. Reinertsen E, Nemati S, Vest AN, Vaccarino V, Lampert R, Shah AJ, Clifford GD. Heart rate-based window segmentation improves accuracy of classifying posttraumatic stress disorder using heart rate variability measures. Physiol Meas. 2017;38:1061.

    Article  Google Scholar 

  3. Liang L, Mao W, Sun W. A feasibility study of deep learning for predicting hemodynamics of human thoracic aorta. J Biomech. 2020;99:109544.

    Article  Google Scholar 

  4. Zhang N, Yang G, Gao Z, Xu C, Zhang Y, Shi R, Keegan J, Xu L, Zhang H, Fan Z, Firmin D. Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI. Radiology. 2019.

    Article  Google Scholar 

  5. Cetin I, Sanroma G, Petersen SE, Napel S, Camara O, Ballester M-AG, Lekadir K. A radiomics approach to computer-aided diagnosis with cardiac cine-MRI. In: International workshop on statistical atlases and computational models of the heart. Springer; 2017. p. 82–90.

  6. Clough JR, Oksuz I, Puyol-Antón E, Ruijsink B, King AP, Schnabel JA. Global and local interpretability for cardiac MRI classification. In: International conference on medical image computing and computer-assisted intervention. Springer; 2019. p. 656–64.

  7. Yang D, Wu P, Tan C, Pohl KM, Axel L, Metaxas D. 3D motion modeling and reconstruction of left ventricle wall in cardiac MRI. In: International conference on functional imaging and modeling of the heart. Springer; 2017. p. 481–92.

  8. Qin C, Bai W, Schlemper J, Petersen SE, Piechnik SK, Neubauer S, Rueckert D. Joint motion estimation and segmentation from undersampled cardiac MR image. In: International workshop on machine learning for medical image reconstruction. Springer; 2018. p. 55–63.

  9. Wong SC, Gatt A, Stamatescu V, McDonnell MD. Understanding data augmentation for classification: when to warp? In: 2016 International conference on digital image computing: techniques and applications (DICTA). IEEE; 2016. p. 1–6.

  10. Perez L, Wang J. The effectiveness of data augmentation in image classification using deep learning. 2017. arXiv preprint

  11. Xu C, Xu L, Ohorodnyk P, Roth M, Chen B, Li S. Contrast agent-free synthesis and segmentation of ischemic heart disease images using progressive sequential causal GANs. Med Image Anal. 2020;62:101668.

    Article  Google Scholar 

  12. Diller G-P, Vahle J, Radke R, Vidal MLB, Fischer AJ, Bauer UMM, Sarikouch S, Berger F, Beerbaum P, Baumgartner H. Utility of deep learning networks for the generation of artificial cardiac magnetic resonance images in congenital heart disease. BMC Med Imaging. 2020;20:1–8.

    Article  Google Scholar 

  13. Litjens G, Ciompi F, Wolterink JM, de Vos BD, Leiner T, Teuwen J, Išgum I. State-of-the-art deep learning in cardiovascular image analysis. JACC Cardiovasc Imaging. 2019;12:1549–65.

    Article  Google Scholar 

  14. Carneiro G, Zheng Y, Xing F, Yang L. Review of deep learning methods in mammography, cardiovascular, and microscopy image analysis. In: Lu L, Zheng Y, Carneiro G, Yang L, editors. Deep learning and convolutional neural networks for medical image computing. Cham: Springer; 2017. p. 11–32.

    Chapter  Google Scholar 

  15. Goodfellow I. NIPS 2016 tutorial: generative adversarial networks. 2016. arXiv preprint

  16. Odena A, Olah C, Shlens J. Conditional image synthesis with auxiliary classifier gans. In: Proceedings of the 34th ICML. 2017. p. 2642–2651.

  17. Karras T, Aila T, Laine S, Lehtinen J. Progressive growing of gans for improved quality, stability, and variation. 2017. arXiv preprint

  18. Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. 2015. arXiv preprint

  19. Schlegl T, Seeböck P, Waldstein SM, et al. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Proceeding of IPMI. 2017. p. 146–157.

  20. Bernard O, Lalande A, Zotti C, et al. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved? IEEE Trans Med Imaging. 2018;37:2514–25.

    Article  Google Scholar 

  21. Gulrajani I, Ahmed F, Arjovsky M, et al. Improved training of wasserstein gans. In: Advances in neural information processing systems. Berlin: Springer; 2017. p. 5767–77.

    Google Scholar 

  22. McInnes L, Healy J, Melville J. Umap: Uniform manifold approximation and projection for dimension reduction. 2018. arXiv preprint

  23. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP, et al. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13:600–12.

    Article  Google Scholar 

  24. Villani C. Optimal transport: old and new. Berlin: Springer; 2008.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Fabio Martínez.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gómez, S., Romo-Bucheli, D. & Martínez, F. A digital cardiac disease biomarker from a generative progressive cardiac cine-MRI representation. Biomed. Eng. Lett. 12, 75–84 (2022).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: