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Basal Slice Detection Using Long-Axis Segmentation for Cardiac Analysis

  • Mahsa PaknezhadEmail author
  • Michael S. Brown
  • Stephanie Marchesseau
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

Estimating blood volume of the left ventricle (LV) in the end-diastolic and end-systolic phases is important in diagnosing cardiovascular diseases. Proper estimation of the volume requires knowledge of which MRI slice contains the topmost basal region of the LV. Automatic basal slice detection has proved challenging; as a result, basal slice detection remains a manual task which is prone to inter-observer variability. This paper presents a novel method that is able to track the basal slice over the whole cardiac cycle. The method was tested on 56 healthy and pathological cases and was able to identify the basal slices similar to experts’ selection for 80 % and 85 % of the cases for end-diastole and end-systole, respectively. This provides a significant improvement over the leading state-of-the-art approach that obtained 59 % and 44 % agreement with experts on the same input.

Keywords

Basal slice Long-axis motion Two-chamber view Long-axis view Cardiac analysis MRI 

References

  1. 1.
    Ben Ayed, I., Punithakumar, K., Li, S., Islam, A., Chong, J.: Left ventricle segmentation via graph cut distribution matching. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 901–909. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Marcus, J.T., Gtte, M.J.W., DeWaal, L.K., Stam, M.R., Van der Geest, R.J., Heethaar, R.M., Van Rossum, A.C.: The influence of through-plane motion on left ventricular volumes measured by magnetic resonance imaging: implications for image acquisition and analysis. J. Cardiovasc. Magn. Reson. 1(1), 1–6 (1999)CrossRefGoogle Scholar
  3. 3.
    Marchesseau, S., Ho, J.X.M., Totman, J.J.: Influence of the short-axis cine acquisition protocol on the cardiac function evaluation: a reproducibility study. Eur. J. Radiol. 3, 60–66 (2016)CrossRefGoogle Scholar
  4. 4.
    Tufvesson, J., Hedstrm, E., Steding-Ehrenborg, K., Carlsson, M., Arheden, H., Heiberg, E.: Validation and development of a new automatic algorithm for time-resolved segmentation of the left ventricle in magnetic resonance imaging. BioMed Res. Int. 970357 (2015)Google Scholar
  5. 5.
    Schulz-Menger, J., Bluemke, D.A., Bremerich, J., Flamm, S.D., Fogel, M.A., Friedrich, M.G., Nagel, E.: Standardized image interpretation and post processing in cardiovascular magnetic resonance: society for cardiovascular magnetic resonance (SCMR). J. Cardiovasc. Magn. Reson. 15(35), 1167–1186 (2013)Google Scholar
  6. 6.
    Heiberg, E., Sjgren, J., Ugander, M., Carlsson, M., Engblom, H., Arheden, H.: Design and validation of segment a freely available software for cardiovascular image analysis. BMC Med. Imaging 10(1) (2010)Google Scholar
  7. 7.
    Lu, X., Jolly, M.-P.: Discriminative context modeling using auxiliary markers for LV landmark detection from a single MR image. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2012. LNCS, vol. 7746, pp. 105–114. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-36961-2_13CrossRefGoogle Scholar
  8. 8.
    Mahapatra, D.: Landmark detection in cardiac MRI using learned local image statistics. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2012. LNCS, vol. 7746, pp. 115–124. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-36961-2_14CrossRefGoogle Scholar
  9. 9.
    Zhuang, X., Rhode, K.S., Razavi, R.S., Hawkes, D.J., Ourselin, S.: A registration-based propagation framework for automatic whole heart segmentation of cardiac MRI. IEEE TMI 29(9), 1612–1625 (2010)Google Scholar
  10. 10.
    Paknezhad, M., Marchesseau, S., Brown, M.S.: Automatic basal slice detection for cardiac analysis. In: SPIE 9784, Medical Imaging: Image Processing (2016)Google Scholar
  11. 11.
    Li, C., Huang, R., Ding, Z., Gatenby, J.C., Metaxas, D.N., Gore, J.C.: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE TIP 20(7), 2007–2016 (2011)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Grady, L.: Random walks for image segmentation. IEEE PAMI 28(11), 1768–1783 (2006)CrossRefGoogle Scholar
  13. 13.
    Verevka, O.: The Local K-means Algorithm for Colour Image Quantization. ProQuest Dissertation Publishing (1995)Google Scholar
  14. 14.
    Myronenko, A., Song, X.: Intensity-based image registration by minimizing residual complexity. IEEE PAMI 29(11), 1882–1891 (2010)Google Scholar

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© Springer International Publishing AG 2016

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Authors and Affiliations

  • Mahsa Paknezhad
    • 1
    Email author
  • Michael S. Brown
    • 2
  • Stephanie Marchesseau
    • 3
  1. 1.National University of Singapore (NUS)SingaporeSingapore
  2. 2.York UniversityYorkCanada
  3. 3.Clinical Imaging Research CentreA*STAR-NUSSingaporeSingapore

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