Experimental validation of an automated edge-detection method for a simultaneous determination of the endocardial and epicardial borders in short-axis cardiac MR images: application in normal volunteers.

The goal of this study was to put together several techniques of image segmentation to provide a reliable assessment of the left ventricular mass with short-axis cardiac MR images. No initial manual input was required for this process based on region growing, gradient detection, and adaptive thresholding. A comparison between actual mass and automatic assessment was implemented with 9 minipigs that underwent spin-echo MR imaging. Fifteen normal volunteers were studied with a fast-gradient-echo sequence. The automatic segmentation was then controlled by three trained observers. Actual mass and automatic segmentation were strongly correlated (r = .97 with P < .01). For normal volunteers, the standard error of estimation of the automatic assessment (12 g) compared well with the average myocardial mass (120 +/- 30 g) and the interobserver reproducibility of the manual assessment (9 g). These results allow the application of this method to the quantification of the left ventricular function and mass in clinical practice.