Segmentation and Registration Coupling from Short-Axis Cine MRI: Application to Infarct Diagnosis

  • Stephanie Marchesseau
  • Nicolas Duchateau
  • Hervé Delingette
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10124)

Abstract

Estimating regional deformation of the myocardium from Cine MRI has the potential to locate abnormal tissue. Regional deformation of the left ventricle is commonly estimated using either segmentation or 3D + t registration. Segmentation is often performed at each instant separately from the others. It can be tedious and does not guarantee temporal causality. On the other hand, extracting regional parameters through image registration is highly dependent on the initial segmentation chosen to propagate the deformation fields and may not be consistent with the myocardial contours. In this paper, we propose an intermediate approach that couples segmentation and registration in order to improve temporal causality while removing the influence of the chosen initial segmentation. We propose to apply the deformation fields from image registration (sparse Bayesian registration) to every segmentation of the cardiac cycle and combine them for more robust regional measurements. As an illustration, we describe local deformation through the measurement of AHA regional volumes. Maximum regional volume change is extracted and compared across scar and non-scar regions defined from delayed enhancement MRI on 20 ST-elevation myocardial infarction patients. The proposed approach shows (i) more robustness in extracting regional volumes than direct segmentation or standard registration and (ii) better performance in detecting scar.

Keywords

Regional volumes Segmentation Registration Infarct diagnosis 

References

  1. 1.
    Albà, X., Figueras i Ventura, R.M., Lekadir, K., Frangi, A.F.: Healthy and scar myocardial tissue classification in DE-MRI. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2012. LNCS, vol. 7746, pp. 62–70. Springer, Heidelberg (2013). doi:10.1007/978-3-642-36961-2_8 CrossRefGoogle Scholar
  2. 2.
    Auger, D., Ducharme, A., Harel, F., Marcotte, F., Thibault, B., O’Meara, E.: Patient assessment for cardiac resynchronization therapy: past, present and future of imaging techniques. Can. J. Cardiol. 26(1), 27–34 (2010)CrossRefGoogle Scholar
  3. 3.
    De Craene, M., Piella, G., Camara, O., Duchateau, N., Silva, E., Doltra, A., et al.: Temporal diffeomorphic free-form deformation: application to motion and strain estimation from 3D echocardiography. Med. Image Anal. 16(2), 427–450 (2012)CrossRefGoogle Scholar
  4. 4.
    Duchateau, N., De Craene, M., Allain, P., Saloux, E., Sermesant, M.: 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
  5. 5.
    Heiberg, E., Engblom, H., Engvall, J., Hedström, E., Ugander, M., Arheden, H.: Semi-automatic quantification of myocardial infarction from delayed contrast enhanced magnetic resonance imaging. Scand. Cardiovasc. J. 39(5), 267–275 (2005)CrossRefGoogle Scholar
  6. 6.
    Karim, R., et al.: Infarct segmentation challenge on delayed enhancement MRI of the left ventricle. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2012. LNCS, vol. 7746, pp. 97–104. Springer, Heidelberg (2013). doi:10.1007/978-3-642-36961-2_12 CrossRefGoogle Scholar
  7. 7.
    Atehortúa Labrador, A.M., Zuluaga, M.A., Ourselin, S., Giraldo, D., Castro, E.R.: Automatic segmentation of 4D cardiac MR images for extraction of ventricular chambers using a spatio-temporal approach. In: SPIE Medical Imaging. International Society for Optics and Photonics (2016)Google Scholar
  8. 8.
    Folgoc, L., Delingette, H., Criminisi, A., Ayache, N.: Sparse bayesian registration. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 235–242. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10404-1_30 Google Scholar
  9. 9.
    Marchesseau, S., Delingette, H., Sermesant, M., Cabrera-Lozoya, R., Tobon-Gomez, C., Moireau, P., et al.: Personalization of a cardiac electromechanical model using reduced order unscented Kalman filtering from regional volumes. Med. Image Anal. 17(7), 816–829 (2013)CrossRefGoogle Scholar
  10. 10.
    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). doi:10.1007/978-3-642-36961-2_22 CrossRefGoogle Scholar
  11. 11.
    Petitjean, C., Dacher, J.N.: A review of segmentation methods in short axis cardiac MR images. Med. Image Anal. 15(2), 169–184 (2011)CrossRefGoogle Scholar
  12. 12.
    Zhang, Z., Ashraf, M., Sahn, D.J., Song, X.: Temporally diffeomorphic cardiac motion estimation from three-dimensional echocardiography by minimization of intensity consistency error. Med. Phys. 41(5), 052902 (2014)CrossRefGoogle Scholar
  13. 13.
    Zhuang, X., Rhode, K., Razavi, R., Hawkes, D., Ourselin, S.: A registration-based propagation framework for automatic whole heart segmentation of cardiac MRI. IEEE Trans. Med. Imaging 29(9), 1612–1625 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Stephanie Marchesseau
    • 1
  • Nicolas Duchateau
    • 2
  • Hervé Delingette
    • 2
  1. 1.Clinical Imaging Research CentreA*STAR-NUSSingaporeSingapore
  2. 2.Asclepios Research Project, Inria Sophia AntipolisValbonneFrance

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