Multiview Machine Learning Using an Atlas of Cardiac Cycle Motion

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10663)


A cardiac motion atlas provides a space of reference in which the cardiac motion fields of a cohort of subjects can be directly compared. From such atlases, descriptors can be learned for subsequent diagnosis and characterization of disease. Traditionally, such atlases have been formed from imaging data acquired using a single modality. In this work we propose a framework for building a multimodal cardiac motion atlas from MR and ultrasound data and incorporate a multiview classifier to exploit the complementary information provided by the two modalities. We demonstrate that our novel framework is able to detect non ischemic dilated cardiomyopathy patients from ultrasound data alone, whilst still exploiting the MR based information from the multimodal atlas. We evaluate two different approaches based on multiview learning to implement the classifier and achieve an improvement in classification performance from 77.5% to 83.50% compared to the use of US data without the multimodal atlas.


Multimodal cardiac motion atlas Multiview dimensionality reduction Classification 


  1. 1.
    Bai, W., Shi, W., et al.: A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion. Med. Image Anal. 26(1), 133–145 (2015)CrossRefGoogle Scholar
  2. 2.
    Duchateau, N., De Craene, M., et al.: Infarct localization from myocardial deformation: prediction and uncertainty quantification by regression from a low-dimensional space. IEEE Trans. Med. Imaging 35(10), 2340–2352 (2016)CrossRefGoogle Scholar
  3. 3.
    Medrano-Gracia, P., Suinesiaputra, A., Cowan, B., Bluemke, D., Frangi, A., Lee, D., Lima, J., Young, A.: An atlas for cardiac MRI regional wall motion and infarct scoring. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2012. LNCS, vol. 7746, pp. 188–197. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  4. 4.
    Peressutti, D., Sinclair, M., et al.: A framework for combining a motion atlas with non-motion information to learn clinically useful biomarkers: application to cardiac resynchronisation therapy response prediction. Med. Image Anal. 35, 669–684 (2017)CrossRefGoogle Scholar
  5. 5.
    Perperidis, D., Mohiaddin, R., Rueckert, D.: Construction of a 4D statistical atlas of the cardiac anatomy and its use in classification. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 402–410. Springer, Heidelberg (2005). CrossRefGoogle Scholar
  6. 6.
    Puyol-Antón, E., Peressutti, D., et al.: Towards a multimodal cardiac motion atlas. In: ISBI, pp. 32–35. IEEE (2016)Google Scholar
  7. 7.
    Puyol-Antón, E., Sinclair, M., Gerber, B., Amzulescu, M., Langet, H., De Craene, M., Aljabar, P., Piro, P., King, A.: A multimodal spatiotemporal cardiac motion atlas from MR and ultrasound data. Med. Image Anal. 40, 96–110 (2017)CrossRefGoogle Scholar
  8. 8.
    Rueckert, D., Sonoda, L., et al.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar
  9. 9.
    Sun, S., Xie, X., Yang, M.: Multiview uncorrelated discriminant analysis. IEEE Trans. Cybern. 18, 712–721 (2015)Google Scholar
  10. 10.
    Tobon-Gomez, C., De Craene, M., et al.: Benchmarking framework for myocardial tracking and deformation algorithms: an open access database. Med. Image Anal. 17(6), 632–648 (2013)CrossRefGoogle Scholar
  11. 11.
    Wold, H.: Partial Least Squares. Encyclopedia of Statistical Sciences. Wiley, New York (1985)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Division of Imaging Sciences and Biomedical EngineeringKing’s College LondonLondonUK
  2. 2.Philips Research, MedisysParisFrance
  3. 3.Division of CardiologyCliniques Universitaires St-LucBrusselsBelgium

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