Semi-automated Processing of Real-Time CMR Scans for Left Ventricle Segmentation

  • Rahil ShahzadEmail author
  • Martin Fasshauer
  • Boudewijn P. F. Lelieveldt
  • Joachim Lotz
  • Rob van der Geest
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10883)


We present a workflow for processing real-time cardiac MR (RT-CMR) scans for segmenting the left ventricle (LV) on short-axis slices (SAX). Our method is based on image registration, where the LV endocardium and epicardium are segmented by propagating a reference contour over all the frames of the RT-CMR SAX scans. Our method was evaluated on 19 subjects, the accuracy of the automatic LV endocardium and epicardium segmentation was compared to those defined manually. The proposed method obtained a dice similarity coefficient (DSC) of 0.94 and a mean surface-to-surface distance (MSD) measure of 0.89 ± 0.53 mm. Additionally, a number of automatically obtained clinical measures were compared to ground truth values. On average we obtained a Pearson’s correlation coefficient (R) of 0.94 (0.99–0.74).


Realtime-MR Left ventricle Segmentation Registration Semi-automatic 


  1. 1.
    Manning, W.J., Pennell, D.J.: Cardiovascular Magnetic Resonance. Elsevier Health Sciences, Philadelphia (2010)Google Scholar
  2. 2.
    Zhang, S., Uecker, M., Voit, D., Merboldt, K.D., Frahm, J.: Real-time cardiovascular magnetic resonance at high temporal resolution: radial FLASH with nonlinear inverse reconstruction. J. Cardiovas. Magn. Reson. 12(1), 39 (2010)CrossRefGoogle Scholar
  3. 3.
    Uecker, M., Zhang, S., Voit, D., Karaus, A., Merboldt, K.D., Frahm, J.: Real-time MRI at a resolution of 20 ms. NMR in Biomed. 23(8), 986–994 (2010)CrossRefGoogle Scholar
  4. 4.
    Sotiras, A., Davatzikos, C., Paragios, N.: Deformable medical image registration: a survey. IEEE Trans. Med. Imaging 32(7), 1153–1190 (2013)CrossRefGoogle Scholar
  5. 5.
    Metz, C., Klein, S., Schaap, M., van Walsum, T., Niessen, W.J.: Nonrigid registration of dynamic medical imaging data using nD+t B-splines and a groupwise optimization approach. Med. Image Anal. 15(2), 238–249 (2011)CrossRefGoogle Scholar
  6. 6.
    Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2010)CrossRefGoogle Scholar
  7. 7.
    Shahzad, R., Tao, Q., Dzyubachyk, O., Staring, M., Lelieveldt, B.P., van der Geest, R.J.: Fully-automatic left ventricular segmentation from long-axis cardiac cine MR scans. Med. Image Anal. 39, 44–55 (2017)CrossRefGoogle Scholar
  8. 8.
    Klein, S., Pluim, J.P., Staring, M., Viergever, M.A.: Adaptive stochastic gradient descent optimisation for image registration. Int. J. Comput. Vis. 81(3), 227 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Rahil Shahzad
    • 1
    Email author
  • Martin Fasshauer
    • 2
  • Boudewijn P. F. Lelieveldt
    • 1
    • 3
  • Joachim Lotz
    • 2
  • Rob van der Geest
    • 1
  1. 1.Division of Image Processing (LKEB), Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
  2. 2.Institute for Diagnostic and Interventional RadiologyUniversity Medical Center GöttingenGöttingenGermany
  3. 3.Intelligent Systems DepartmentDelft University of TechnologyDelftThe Netherlands

Personalised recommendations