Abstract
Delayed-enhancement cardiac magnetic resonance (DE-CMR) provides important diagnostic and prognostic information on myocardial viability. The presence and extent of late gadolinium enhancement (LGE) in DE-CMR is negatively associated with the probability of improvement in left ventricular function after revascularization. Moreover, LGE findings can support the diagnosis of several other cardiomyopathies, but their absence does not rule them out, making disease classification by visual assessment difficult. In this work, we propose deep learning neural networks that can automatically predict myocardial disease from patient clinical information and DE-CMR. All the proposed networks achieved very good classification accuracy (>85%). Including information from DE-CMR (directly as images or as metadata following DE-CMR segmentation) is valuable in this classification task, improving the accuracy to 95–100%.
M. Varela and T.M. Correia—Contributed equally.
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
EMIDEC classification challenge (2020). http://emidec.com/classification-contest
Allman, K., et al.: Myocardial viability testing and impact of revascularization on prognosis in patients with coronary artery disease and left ventricular dysfunction: a meta-analysis. J. Am. Coll. Cardiol. 39(7), 1151–8 (2002)
Arai, A.: The cardiac magnetic resonance (CMR) approach to assessing myocardial viability. J. Nucl. Cardiol. 18(6), 1095–1102 (2011)
Baeßler, B., et al.: Mapping tissue inhomogeneity in acute myocarditis: a novel analytical approach to quantitative myocardial edema imaging by T2-mapping. J. Cardiovasc. Magn. Reson. 17(1), 115 (2015)
Bettencourt, N., Chiribiri, A., Schuster, A., Nagel, E.: Assessment of myocardial ischemia and viability using cardiac magnetic resonance. Curr. Heart Fail Rep. 6(3), 142–153 (2009)
Bonow, R., et al.: Myocardial viability and survival in ischemic left ventricular dysfunction. N. Engl. J. Med. 364(17), 1617–25 (2011)
Gerber, B., et al.: Prognostic value of myocardial viability by delayed-enhanced magnetic resonance in patients with coronary artery disease and low ejection fraction: impact of revascularization therapy. J. Am. Coll. Cardiol. 59(9), 825–35 (2012)
Kerfoot, E., Puyol Anton, E., Ruijsink, B., Clough, J., King, A.P., Schnabel, J.A.: Automated CNN-based reconstruction of short-axis cardiac MR sequence from real-time image data. In: Stoyanov, D., et al. (eds.) RAMBO/BIA/TIA -2018. LNCS, vol. 11040, pp. 32–41. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00946-5_4
Kim, R., et al.: The use of contrast-enhanced magnetic resonance imaging to identify reversible myocardial dysfunction. N. Engl. J. Med. 343(20), 1445–53 (2000)
Kramer, C., et al.: Role of cardiac MR imaging in cardiomyopathies. J. Nucl. Med. 56, 39S–45S (2015)
Lalande, A., et al.: Emidec: a database usable for the automatic evaluation of myocardial infarction from delayed-enhancement cardiac MRI. Data 5(4), 89 (2020)
Lee, E., et al.: Practical guide to evaluating myocardial disease by cardiac MRI. Am. J. Roentgenol. 214(3), 546–556 (2020)
Leiner, T., et al.: Machine learning in cardiovascular magnetic resonance: basic concepts and applications. J. Cardiovasc. Magn. Reson. 21(1), 61 (2019)
Lourenço, A., et al.: Left atrial ejection fraction estimation using SEGANet for fully automated segmentation of CINE MRI (2020)
Mantilla, J., et al.: Detection of fibrosis in late gadolinium enhancement cardiac MRI using kernel dictionary learning-based clustering. In: Computing in Cardiology Conference (CinC), pp. 357–360 (2015)
Narula, S., et al.: Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J. Am. Coll. Cardiol. 68(21), 2287–95 (2016)
Soriano, C., et al.: Noninvasive diagnosis of coronary artery disease in patients with heart failure and systolic dysfunction of uncertain etiology using late gadolinium-enhanced cardiovascular magnetic resonance. J. Am. Coll. Cardiol. 45(5), 743–48 (2005)
Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 240–248. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_28
Weinsaft, J., Klem, I., Judd, R.: MRI for the assessment of myocardial viability. Cardiol. Clin. 25(1), 35–36 (2007)
Acknowledgments
This work was supported by the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z] and the British Heart Foundation Centre of Research Excellence at Imperial College London [RE/18/4/34215].
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Lourenço, A., Kerfoot, E., Grigorescu, I., Scannell, C.M., Varela, M., Correia, T.M. (2021). Automatic Myocardial Disease Prediction from Delayed-Enhancement Cardiac MRI and Clinical Information. 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. https://doi.org/10.1007/978-3-030-68107-4_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-68107-4_34
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)
