Automated Detection of the Left Ventricle from 4D MR Images: Validation Using Large Clinical Datasets

  • Xiang Lin
  • Brett Cowan
  • Alistair Young
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)


We present a fully automated method to estimate the location and orientation of the left ventricle (LV) from four-dimensional (4D) cardiac magnetic resonance (CMR) images without requiring user input. The method is based on low-level image processing techniques which incorporate anatomical knowledge and is able to provide rapid, robust feedback for automated scan planning or further processing. The method relies on a novel combination of temporal Fourier analysis of image cines and simple contour detection to achieve a fast localization of the heart. Quantitative validation was performed using two 4D CMR datasets containing 395 patients (63720 images), with a range of cardiac and vascular disease, by comparing manual location with the automatic results. The method failed in only one case, and showed an average bias of better than 5mm in the apical, mid-ventricular and basal slices in the remaining 394. The errors in the automatically detected LV orientation were similar to those found in scan planning when performed by experienced technicians.


Cardiac Magnetic Resonance Right Ventricle Short Axis Slice Cardiac Magnetic Resonance Examination Middle Slice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Santarelli, M., Positano, V., Michelassi, C., Lombardi, M., Landini, L.: Automated cardiac MR image segmentation: theory and measurement evaluation. Med. Eng. Phys. 25, 149–159 (2003)CrossRefGoogle Scholar
  2. 2.
    Lorenzo-Valdés, M., Sanchez-Ortiz, G., Mohiaddin, R., Rueckert, D.: Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 440–450. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Mitchell, S., Bosch, J., Lelieveldt, B., van der Geest, R., Reiber, J., Sonka, M.: 3-D active appearance models: Segmentation of cardiac MR and ultrasound images. IEEE Trans. Med. Imag. 21(9), 1167–1178 (2002)CrossRefGoogle Scholar
  4. 4.
    Kaus, M.R., von Berg, J., Niessen, W., Pekar, V.: Automated segmentation of the left ventricle in cardiac MRI. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 432–439. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Montillo, A., Metaxas, D., Axel, L.: Automated segmentation of the left and right ventricles in cardiac SPAMM images. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 620–633. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Spreeuwers, L., Breeuwer, M.: Automatic detection of the myocardial boundaries of the right and left ventricle. SPIE: Med. Imag. 4322, 1207–1217 (2001)Google Scholar
  7. 7.
    Danilouchkine, M., Westenberg, J., Reiber, J., Lelieveldt, B.: Accuracy of short-axis cardiac MRI automatically derived from scout acquisitions in free-breathing and breath-holding modes. MAGMA 18, 7–18 (2005)CrossRefGoogle Scholar
  8. 8.
    Jackson, C., Robson, M., Francis, J., Noble, J.: Automatic planning of the acquisition of cardiac MR images. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 541–548. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Anderson, C.: Rationale and design of the cardiac magnetic resonance imaging substudy of the ONTARGET trial programme. J. Int. Med. Res. 33(4), 50–57 (2005)Google Scholar
  10. 10.
    Young, A., Cowan, B., Thrupp, S., Hedley, W., Dell’Italia, L.: Left ventricular mass and volume: fast calculation with guide-point modeling on MR images. Radiology 216, 597–602 (2000)Google Scholar
  11. 11.
    Sörgel, W., Vaerman, V.: Automatic heart localization from 4D MRI datasets. SPIE: Med. Imag. 3034, 333–344 (1997)Google Scholar
  12. 12.
    Gering, D.: Automatic segmentation of cardiac MRI. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 524–532. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiang Lin
    • 1
  • Brett Cowan
    • 2
  • Alistair Young
    • 1
    • 2
  1. 1.Bioengineering InstituteUniversity of AucklandNew Zealand
  2. 2.Center for Advanced MRIUniversity of AucklandNew Zealand

Personalised recommendations