Multivariate Mixture Model for Cardiac Segmentation from Multi-Sequence MRI

Cardiac segmentation is commonly a prerequisite for functional analysis of the heart, such as to identify and quantify the infarcts and edema from the normal myocardium using the late-enhanced (LE) and T2-weighted MRI. The automatic delineation of myocardium is however challenging due to the heterogeneous intensity distributions and indistinct boundaries in the images. In this work, we present a multivariate mixture model (MvMM) for text classification, which combines the complementary information from multi-sequence (MS) cardiac MRI and perform the segmentation of them simultaneously. The expectation maximization (EM) method is adopted to estimate the segmentation and model parameters from the log-likelihood (LL) of the mixture model, where a probabilistic atlas is used for initialization. Furthermore, to correct the intra- and inter-image misalignments, we formulate the MvMM with transformations, which are embedded into the LL framework and thus can be optimized by the iterative conditional mode approach. We applied MvMM for segmentation of eighteen subjects with three sequences and obtained promising results. We compared with two conventional methods, and the improvements of segmentation performance on LE and T2 MRI were evident and statistically significant by MvMM.