Automated segmentation of left ventricle in cine cardiac mr images
Journal of Cardiovascular Magnetic Resonance volume 12, Article number: P238 (2010)
To quantitatively analyze global and regional cardiac function from MR, clinical parameters such as ejection fraction (EF) and volumes are required. These depend upon accurate delineation of endo- and epicardial contours of the left ventricle (LV). Accurate LV segmentation is acknowledged as a difficult problem because of the overlap between the intensity distributions within the cardiac regions, the lack of edge information, and the inter-subject variability. A novel method for the robust, accurate and fully automatic LV segmentation from short axis (SA) cine MR images is presented in this study.
Materials and methods
Imaging data (N = 153, 40 ischemic heart failure, 34 non-ischemic heart failure, 39 LV hypertrophy and 40 normals) were acquired from a 1.5 T scanner (GE CV/i Excite) with IR-SSFP SA cine MR. The segmentation algorithm consists of three stages for each data set. First, the LV centre is localized on a mid-ventricular slice image in the end-diastolic phase (starting image) by a roundness metric (Fig. 1). Second, the endocardial contour is detected by determining an optimal threshold and binary blood pool image, then the endocardial contour is smoothed by applying the fast Fourier transform (Fig. 2). Third, the epicardial contour is detected by mapping the pixels from Cartesian to polar coordinates, then region growing is used to get the contour. The contour is then smoothed by the fast Fourier transform (Fig. 3).
The accuracy of LV location is 94.1%(144/153). The average computation time of LV location is 0.085 ± 0.013 s per subject. The average perpendicular distance (APD) between the detected and the manually drawn expert contours and Dice metric (DM) over slices and ES and ED phases are shown in Table 1. The average computation time of the segmentation is about 0.8 s per image for these 153 exams.
Discussion and conclusion
In summary, the proposed fully automated segmentation technique is fast, accurate and robust and should be of benefit for quantification of cine cardiac MR in clinical practice.
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Cite this article
Lu, Y., Radau, P., Connelly, K.A. et al. Automated segmentation of left ventricle in cine cardiac mr images. J Cardiovasc Magn Reson 12 (Suppl 1), P238 (2010). https://doi.org/10.1186/1532-429X-12-S1-P238