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

Abstract

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.

In vivo MRI quantification of individual muscle and organ volumes for assessment of anabolic steroid growth effects

“…To facilitate the rapid tissue segmentation and quantification, several functions were incorporated into a graphical user interface. They included (i) multislice and multi-planar visualization of MRI data in standard formats including DICOM format; (ii) coarse histogram-based semi-automatic segmentation of local structures (muscle, fat and organs) by selecting specific range of image intensities within the image intensity histogram [8]; (iii) fine segmentation by 2D and 3D region growing from the initial histogram-based segmentation results to take advantage of tissue connectivity and structural similarity within the same or adjacent image slices [26]; (iv) manual segmentation and correction of segmentation errors; (v) labeling of various segmented structures by different colors; and (vi) volume and mass estimation of segmented structures by voxel sizes, numbers and densities assumed; (vii) data output to the standardized EXCEL files for statistical analysis. The integration of these seven functions into a single graphical interface led to minimal operator interaction and much improved segmentation efficiency, with flexibility to accommodate certain level of image quality variations that were slight but expected in high throughput MRI experiments.…”

3-D MRI assessment of regional left ventricular systolic wall stress in patients with reperfused MI

“…The endocardial and epicardial borders of the left ventricle were drawn with an automatic segmentation method previously validated in animals and patients (1,8).…”

“…The geometry of the left ventricle in infarction is complicated, and only a tridimensional approach, excluding the use of a geometric model, is valid in the presence of ventricular deformations, which can occur after myocardial infarction. Because of good spatial resolution and an absence of a geometric hypothesis, the MRI allows precise definition of the epicardial and endocardial borders of the left ventricle (1,8).Several mathematical models have been used to calculate the wall stress. Some (10) depend on the shape of the left ventricle and are only valid in the presence of a ventricle of spheroid or ellipsoid shape and only allow calculation of the global wall stress.…”

“…The geometry of the left ventricle in infarction is complicated, and only a tridimensional approach, excluding the use of a geometric model, is valid in the presence of ventricular deformations, which can occur after myocardial infarction. Because of good spatial resolution and an absence of a geometric hypothesis, the MRI allows precise definition of the epicardial and endocardial borders of the left ventricle (1,8).…”

Cardiac Function

“…Another impediment is the discrimination between atria and ventricles because of difficulties in defining the atrioventricular valve position. Different methods have been reported for automatic segmentation of the endocardial border, including region growing, edge detection, adaptive thresholding, fuzzy logic, volumetric surface detection, and threedimensional information for contour detection (Singelton and Pohost 1997;Furber et al 1998;Baldy et al 1994;Lalande et al 1999;Santaralli et al 2003;Corsi et al 2005;van Geuns et al 2006;Codella et al 2008). Results have been ambiguous.…”