Myocardial Segmentation of Contrast Echocardiograms Using Random Forests Guided by Shape Model

TL;DR

This paper introduces a novel segmentation method combining statistical shape models with Random Forests to improve myocardial segmentation accuracy in contrast echocardiograms, aiding early coronary artery disease detection.

Contribution

The paper presents Shape Model guided Random Forests (SMRF), integrating shape priors into RF for more accurate myocardial segmentation in MCE images.

Findings

Achieved Dice coefficient of 0.81

Improved segmentation accuracy over classic RF

Demonstrated robustness on clinical data

Abstract

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.