Myocardium segmentation combining T2 and DE MRI using Multi-Component Bivariate Gaussian mixture model

Jie Liu, Xiahai Zhuang, Jing Liu, Shaoting Zhang, Guotai Wang, Lianming Wu, Jianrong Xu, Lixu Gu

Abstract: Accurately delineating the myocardium from cardiac T2 and delayed enhanced (DE) MRI is a prerequisite to identifying and quantifying the edema and infarcts. The automatic delineation is however challenging due to the heterogeneous intensity distribution of the myocardium. In this paper, we propose a fully automatic method, which combines the complementary information from the two sequences using the newly proposed Multi-Component Bivariate Gaussian (MCBG) mixture model. The expectation maximization (EM) framework is adopted to estimate the segmentation and model parameters, where a probabilistic atlas is also used. This method performs the segmentation on the two MRI sequences simultaneously, and hence improves the robustness and accuracy. The results on six clinical cases showed that the proposed method significantly improved the performance compared to the atlas-based methods: myocardium Dice scores 0.643±0.084 versus 0.576±0.103 (P=0.002) on DE MRI, and 0.623±0.129 versus 0.484±0.106 (P=0.002) on T2 MRI.