  Myocardial contrast echocardiography (MCE) is an imaging technique that
assesses left ventricle function and myocardial perfusion for the detection of
coronary artery diseases. Automatic MCE perfusion quantification is challenging
and requires accurate segmentation of the myocardium from noisy and
time-varying images. Random forests (RF) have been successfully applied to many
medical image segmentation tasks. However, the pixel-wise RF classifier ignores
contextual relationships between label outputs of individual pixels. RF which
only utilizes local appearance features is also susceptible to data suffering
from large intensity variations. In this paper, we demonstrate how to overcome
the above limitations of classic RF by presenting a fully automatic
segmentation pipeline for myocardial segmentation in full-cycle 2D MCE data.
Specifically, a statistical shape model is used to provide shape prior
information that guide the RF segmentation in two ways. First, a novel shape
model (SM) feature is incorporated into the RF framework to generate a more
accurate RF probability map. Second, the shape model is fitted to the RF
probability map to refine and constrain the final segmentation to plausible
myocardial shapes. We further improve the performance by introducing a bounding
box detection algorithm as a preprocessing step in the segmentation pipeline.
Our approach on 2D image is further extended to 2D+t sequence which ensures
temporal consistency in the resultant sequence segmentations. When evaluated on
clinical MCE data, our proposed method achieves notable improvement in
segmentation accuracy and outperforms other state-of-the-art methods including
the classic RF and its variants, active shape model and image registration.
