Fully automated endocardial contour detection in time sequences of echocardiograms by active appearance motion models
Johan G. Bosch, Susan Mitchell, Boudewijn P. F. Lelieveldt, Francisca Nijland, Otto Kamp, Milan Sonka, Johan H. C. Reiber · 2002
A novel fully automated border detection technique for phase-normalized echocardiographic image sequences is developed: Active Appearance-Motion Models (AAMM). AAMM finds shape and appearance eigenvariations of the heart over the full cardiac cycle from a set of examples, capturing typical motion patterns. AAMM segments sequences by adjusting eigenvariation coefficients to minimize model-to-target differences. This results in a time-continuous segmentation. The method was applied on 4-chamber sequences from 129 unselected patients, split randomly into training (TRN, n=65) and test set (TST, n=64). In all sequences, an independent expert manually drew endocardial contours (MAN). On TST, fully automated AAMM succeeded in 97% of cases (AUTO) and performed well (average contour distance 3.3 mm, area regression AUTO=0.91 *MAN+1.7 cm/sup 2/, r=0.87). Results outperformed single-frame AAM segmentation and human interobserver variabilities.