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A novel 3-D Active Appearance Model (3-D AAM) is applied to fully automated endocardial contour detection in 2-D + time (2DT) 4-chamber ultrasound sequences, without knowledge of cardiac phase (ED/ES frames). 2DT appearance of the heart is modeled in 3-D by converting the stack of 2-D time slices into a 3-D voxel space. In a training set, an expert defines corresponding endocardial contour points for one complete cardiac cycle (ED to ED). 2DT shape is represented as a 3-D surface. Image appearance is modeled as a vector of voxel intensities in a volume-patch spanned by the 3-D surface. Principal Component Analysis extracts eigenvariations of 3-D shape and appearance, capturing typical cardiac motion patterns. 3-D AAM segments the image volume by minimizing 3-D model-to-target intensity differences, adjusting eigenvariation coefficients and 3-D pose using gradient descent minimization. This provides time-continuous border localization for one beat in both time and space. The method was used on 3-beat sequences from 129 patients split randomly into a training (65) and a test set (64). An independent expert manually drew all endocardial contours. 3-D AAM converged well in 89% of test cases. Average absolute temporal error was 37.0 msec, spatial error 3.35 mm, comparable to human inter-observer variabilities.
Bosch et al. (Wed,) studied this question.