  Myocardial Contrast Echocardiography (MCE) with micro-bubble contrast agent
enables myocardial perfusion quantification which is invaluable for the early
detection of coronary artery diseases. In this paper, we proposed a new
segmentation method called Shape Model guided Random Forests (SMRF) for the
analysis of MCE data. The proposed method utilizes a statistical shape model of
the myocardium to guide the Random Forest (RF) segmentation in two ways. First,
we introduce a novel Shape Model (SM) feature which captures the global
structure and shape of the myocardium to produce a more accurate RF probability
map. Second, the shape model is fitted to the RF probability map to further
refine and constrain the final segmentation to plausible myocardial shapes.
Evaluated on clinical MCE images from 15 patients, our method obtained
promising results (Dice=0.81, Jaccard=0.70, MAD=1.68 mm, HD=6.53 mm) and showed
a notable improvement in segmentation accuracy over the classic RF and its
variants.
