Abstract
The use of human embryonic stem cell-derived cardiomyocytes (hESC-CMs) in applications such as cardiac regenerative medicine requires understanding them in the context of adult CMs. Their classification in terms of the major adult CM phenotypes is a crucial step to build this understanding. However, this is a challenging problem due to the lack of labels for hESC-CMs. Adult CM phenotypes are easily distinguishable based on the shape of their action potentials (APs), but it is still unclear how these phenotypes are expressed in the APs of hESC-CM populations. Recently, a metamorphosis distance was proposed to measure similarities between hESC-CM APs and adult CM APs, which led to state-of-the-art performance when used in a 1 nearest neighbor scheme. However, its computation is prohibitively expensive for large datasets. A recurrent neural network (RNN) classifier was recently shown to be computationally more efficient than the metamorphosis-based method, but at the expense of accuracy. In this paper we argue that the APs of adult CMs and hESC-CMs intrinsically belong to different domains, and propose an unsupervised domain adaptation approach to train the RNN classifier. The idea is to capture the domain shift between hESC-CMs and adult CMs by adding a term to the loss function that penalizes their maximum mean discrepancy (MMD) in feature space. Experimental results in an unlabeled 6940 hESC-CM dataset show that our approach outperforms the state of the art in terms of both clustering quality and computational efficiency. Moreover, it achieves state-of-the-art classification accuracy in a completely different dataset without retraining, which demonstrates the generalization capacity of the proposed method.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
World Health Organization: World Health Statistics 2018: Monitoring health for the Sustainable Development Goals (2018)
Hartman, M.E., Chong, J.J.H., Laflamme, M.A.: State of the art in cardiomyocyte transplantation. In: Ieda, M., Zimmermann, W.-H. (eds.) Cardiac Regeneration. CVB, pp. 177–218. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56106-6_9
Kehat, I., et al.: Human embryonic stem cells can differentiate into myocytes with structural and functional properties of cardiomyocytes. J. Clin. Invest. 108(3), 407–414 (2001)
He, J.Q., Ma, Y., Lee, Y., Thomson, J.A., Kamp, T.J.: Human embryonic stem cells develop into multiple types of cardiac myocytes: action potential characterization. Circ. Res. 93(1), 32–39 (2003)
Sartiani, L., Bettiol, E., Stillitano, F., Mugelli, A., Cerbai, E., Jaconi, M.: Developmental changes in cardiomyocytes differentiated from human embryonic stem cells: a molecular and electrophysiological approach. Stem Cells 25(5), 1136–1144 (2007)
Zhu, R., Millrod, M.A., Zambidis, E.T., Tung, L.: Variability of action potentials within and among cardiac cell clusters derived from human embryonic stem cells. Sci. Rep. 6, 18544 (2016)
Gorospe, G., Younes, L., Tung, L., Vidal, R.: A metamorphosis distance for embryonic cardiac action potential interpolation and classification. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 469–476. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40811-3_59
Gorospe, G., Zhu, R., He, J.Q., Tung, L., Younes, L., Vidal, R.: Efficient metamorphosis computation for classifying embryonic cardiac action potentials. In: 5th Workshop on Mathematical Foundations of Computational Anatomy (2015)
Pacheco, C., Vidal, R.: Recurrent neural networks for classifying human embryonic stem cell-derived cardiomyocytes. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 581–589. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_66
Margolis, A.: A literature review of domain adaptation with unlabeled data. Technical report, pp. 1–42 (2011)
Gretton, A., Borgwardt, K.M., Rasch, M., Schölkopf, B., Smola, A.J.: A kernel method for the two-sample-problem. In: Advances in Neural Information Processing Systems, pp. 513–520 (2007)
Pan, S.J., Kwok, J.T., Yang, Q.: Transfer learning via dimensionality reduction. In: AAAI Conference on Artificial Intelligence, vol. 8, pp. 677–682 (2008)
Duan, L., Tsang, I.W., Xu, D., Maybank, S.J.: Domain transfer SVM for video concept detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1375–1381 (2009)
Dziugaite, G., Roy, D., Ghahramani, Z.: Training generative neural networks via maximum mean discrepancy optimization. In: The Conference on Uncertainty in Artificial Intelligence, pp. 258–267 (2015)
Chen, H.Y., Chien, J.T.: Deep semi-supervised learning for domain adaptation. In: IEEE International Workshop on Machine Learning and Signal Processing, pp. 1–6. IEEE (2015)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. (2), 224–227 (1979)
Nygren, A., et al.: Mathematical model of an adult human atrial cell: the role of K+ currents in repolarization. Circ. Res. 82(1), 63–81 (1998)
O’Hara, T., Virág, L., Varró, A., Rudy, Y.: Simulation of the undiseased human cardiac ventricular action potential: model formulation and experimental validation. PLoS Comput. Biol. 7(5), e1002061 (2011)
Iravanian, S., Tung, L.: A novel algorithm for cardiac biosignal filtering based on filtered residue method. IEEE Trans. Biomed. Eng. 49(11), 1310–1317 (2002)
Chollet, F., et al.: Keras (2015). https://keras.io
Acknowlegement
This work has been supported by NIH #5R01HD87133. The authors thank Dr. Giann Gorospe for insightful discussions, and Dr. Renjun Zhu and Prof. Leslie Tung for providing the hESC-CMs dataset.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Pacheco, C., Vidal, R. (2019). An Unsupervised Domain Adaptation Approach to Classification of Stem Cell-Derived Cardiomyocytes. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_89
Download citation
DOI: https://doi.org/10.1007/978-3-030-32239-7_89
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32238-0
Online ISBN: 978-3-030-32239-7
eBook Packages: Computer ScienceComputer Science (R0)