Abstract
The aim of this study is to create an automatic framework for sustained ventricular arrhythmia (VA) prediction using cardiac computed tomography (CT) images. We built an image processing pipeline and a deep learning network to explore the relation between post-infarct left ventricular myocardium thickness and previous occurrence of VA. Our pipeline generated a 2D myocardium thickness map (TM) from the 3D imaging input. Our network consisted of a conditional variational autoencoder (CVAE) and a classifier model. The CVAE was used to compress the TM into a low dimensional latent space, then the classifier utilised the latent variables to predict between healthy and VA patient. We studied the network on a large clinical database of 504 healthy and 182 VA patients. Using our method, we achieved a mean classification accuracy of \(75\% \pm 4\) on the testing dataset, compared to \(71\% \pm 4\) from the classification using the classical left ventricular ejection fraction (LVEF).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abbet, C., Zlobec, I., Bozorgtabar, B., Thiran, J.-P.: Divide-and-rule: self-supervised learning for survival analysis in colorectal cancer. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 480–489. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59722-1_46
Biffi, C., et al.: Learning interpretable anatomical features through deep generative models: application to cardiac remodeling. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 464–471. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_52
Cedilnik, N., et al: Fast personalized electrophysiological models from CT images for ventricular tachycardia ablation planning. EP-Europace 20(3), iii94-iii101 (2018)
Cedilnik, N., Duchateau, J., Sacher, F., Jaïs, P., Cochet, H., Sermesant, M.: Fully automated electrophysiological model personalisation framework from CT Imaging. In: FIMH 2019–10th International Conference on Functional Imaging and Modeling of the Heart, pp. 325–333. Bordeaux, France (June 2019)
Chattopadhay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks. In: Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018, pp. 839–847 (2018)
Dercksen, K., Bulten, W., Litjens, G.: Dealing with label scarcity in computational pathology: A use case in prostate cancer classification. arXiv (2019)
Efron, B., Rogosa, D., Tibshirani, R.: Resampling Methods of Estimation, vol. 19. Elsevier, second edition edn. (2015)
Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. In: An Introduction to the Bootstrap. CRC Monographs on Statistics and Applied Probability, CRC Press (1993)
Mahida, S., et al.: Cardiac imaging in patients with ventricular tachycardia. Circulation 136(25), 2491–2507 (2017)
Nielsen, J.C., et al.: European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) expert consensus on risk assessment in cardiac arrhythmias: Use the right tool for the right outcome. Europace (2020)
Nuñez-Garcia, M., Cedilnik, N., Jia, S., Sermesant, M., Cochet, H.: Automatic multiplanar CT reformatting from trans-axial into left ventricle short-axis view. In: STACOM 2020–11th International Workshop on Statistical Atlases and Computational Models of the Heart. Lima, Peru (October 2020)
NuÃez-Garcia, M., et al.: Fast quasi-conformal regional flattening of the left atrium. IEEE Trans. Vis. Comput. Graph. 26(8), 2591–2602 (2020)
Sohn, K., Yan, X., Lee, H.: Learning structured output representation using deep conditional generative models. In: Advances in Neural Information Processing Systems, pp. 3483–3491 (2015)
Yezzi, A.J., Prince, J.L.: An Eulerian PDE approach for computing tissue thickness. IEEE Trans. Med. Imaging 22(10), 1332–1339 (2003)
Acknowledgement
Part of the authors’ work has been supported by the French Government, through the National Research Agency (ANR) 3IA Côte d’Azur (ANR-19-P3IA-0002), IHU Liryc (ANR- 10-IAHU-04).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Ly, B., Finsterbach, S., Nuñez-Garcia, M., Cochet, H., Sermesant, M. (2021). Scar-Related Ventricular Arrhythmia Prediction from Imaging Using Explainable Deep Learning. In: Ennis, D.B., Perotti, L.E., Wang, V.Y. (eds) Functional Imaging and Modeling of the Heart. FIMH 2021. Lecture Notes in Computer Science(), vol 12738. Springer, Cham. https://doi.org/10.1007/978-3-030-78710-3_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-78710-3_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78709-7
Online ISBN: 978-3-030-78710-3
eBook Packages: Computer ScienceComputer Science (R0)