Myocardial Segmentation of Contrast Echocardiograms Using Random Forests Guided by Shape Model
This incremental improvement aids in early detection of coronary artery diseases by enhancing segmentation accuracy for medical imaging analysis.
The paper tackled myocardial segmentation in contrast echocardiograms by proposing a shape model-guided random forests method, achieving Dice=0.81 and Jaccard=0.70 on clinical data from 15 patients.
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.