Active Appearance Models (AAM) are suitable for segmenting 2D images, but for image sequences time-continuous results are desired. Active Appearance-Motion Models (AAMM) model shape and appearance of the heart over the full cardiac cycle. Single-beat sequences are phase-normalized into stacks of 16 2D images. In a training set, corresponding shape points on the endocard are defined for each image based on expert drawn contours. Shape (2D) and intensity vectors are derived similar to AAM. Intensities are normalized non-linearly to handle ultrasound-specific problems. For all time frames, shape vectors are simply concatenated, as well as intensity vectors. Principal Component Analysis extracts appearance eigenvariations over the cycle, capturing typical motion patterns. AAMMs perform segmentation on complete sequences by minimizing model-to-target differences, adjusting AAMM eigenvariation coefficients using gradient descent minimization. This results in time-continuous segmentation. The method was trained and tested on echocardiographic 4-chamber sequences of 129 unselected patients split randomly into a training set (n=65) and a test set (n=64). In all sequences, an independent expert manually drew endocardial contours. On the test set, fully automated AAMM performed well in 97% of cases (average distance 3.3 mm, below human interobserver variabilities).
