3D Segmentation of the Left Ventricle Combining Long- and Short-axis MR Images

Summary

Objectives: Segmentation of the left ventricle (LV) is required to quantify LV remodeling after myocardial infarction. Therefore spatiotemporal cine MR sequences including long-axis and short-axis images are acquired. In this paper a new segmentation method for fast and robust segmentation of the left ventricle is presented.

Methods: The new approach considers the position of the mitral valve and the apex as well as the long-axis contours to generate a 3D LV surface model. The segmentation result can be checked and adjusted in the short-axis images. Finally quantitative parameters were extracted.

Results: For evaluation the LV was segmented in eight datasets of the same subject by two medical experts using a contour drawing tool and the new segmentation tool. The results of both methods were compared concerning interaction time and intra- and inter-observer variance. The presented segmentation method proved to be fast. The mean difference and standard deviation of all parameters are decreased. In case of intra-observer comparison e.g. the mean ESV difference is reduced from 8.8% to 0.5%.

Conclusion: A semi-automatic LV segmentation method has been developed that combines long- and short-axis views. Using the presented approach the intra- and inter-observer difference as well as the time for the segmentation process are decreased. So the semi-automatic segmentation using long-and short-axis information proved to be fast and robust for the quantification of LV mass and volume properties.

Keywords

Image processing - myocardial infarct - magnetic resonance imaging cine - left ventricular remodeling - computer-assisted image analysis

• References

• 1 Ehrhardt J, Werner R, Frenzel T, Säring D, Lu W, Low D, Handels H. Optical Flow based Method for Improved Reconstruction of 4D CT Data Sets Acquired During Free Breathing.. Medical Physics 2007; 34 (Suppl. 02) 711-721.
• 2 Handels H, Horsch A, Meinzer H-P. Advances in Medical Image Computing.. Methods Inf Med 2007; 46: 251-253.
• 3 Werner R, Ehrhardt J, Frenzel T, Säring D, Lu W, Low D. et al. Motion Artifact Reducing Reconstruction of 4D CT Image Data for the Analysis of Respiratory Dynamics.. Methods Inf Med 2007; 46: 254-260.
• 4 Säring D, Ehrhardt J, Stork A, Bansmann MP, Lund GK, Handels H. Computer-assisted analysis of 4D cardiac MR image sequences after myocardial infarction.. Methods Inf Med 2006; 45 (Suppl. 04) 377-383.
• 5 Säring D, Ehrhardt J, Stork A, Bannsmann PM, Lund GK, Handels H. HeAT – Heart Analysis Tool. In: 50. Jahrestagung der Deutschen Gesellschaft für Medizinische, Informatik Biometrie und Epidemiologie 2005 pp 28-30.
• 6 Rueckert D, Burger P. Shape-based segmentation and tracking in 4D cardiac MR images. In: CVRMed 1997 pp 43-52.
• 7 Spreeuwers LJ, Breeuwer M. Myocardial boundary extraction using coupled active, contours. In:Computers in Cardiology 2003 pp 745-748.
• 8 Goshtasby AA, Turner DA. Fusion of short-axis and long-axis cardiac MR images.. Comput Med Imaging Graph 1996; 20 (Suppl. 02) 77-87.
• 9 van Geuns RJM, Baks T, Gronenschild EHBM, Aben JPMM, Wielopolski PA, Cademartiri F, de Feyter PJ. Automatic quantitative left ventricular analysis of cine MR images by using three-dimensional information for contour detection.. Radiology 2006; 240: 215-221.
• 10 Segars W, Lalush DS, Tsui BMW. A realistic spline-based dynamic heart phantom.. Nuclear Science, IEEE Transactions 1999; 46 (Suppl. 03) 503-506.
• 11 Ehrhardt J, Säring D, Handels H. Structure-preserving Interpolation of Temporal and Spatial Image Sequences using an Optical Flow-based Method..