Structured graph regularized shape prior and cross-entropy induced active contour model for myocardium segmentation in CTA images

Abstract

Accurate segmentation of myocardium tissue from computed tomography angiography (CTA) images is a crucial step in development of cardiac clinical applications. Automatic methods are highly desirable to release the burden of manual delineation, however, it is challenging due to the presence of images with intensity non-uniformity and coarse, weak and even pseudo boundary. To this end, a new geometric active contour (GAC) model that integrates high-level shape prior and low-level local intensity statistics is proposed. First, the cardiac-type specific shape prior is learned by structured graph-regularized principal component analysis, so that allows to be faithful to the shape of the desired myocardium. Second, local intensity distribution with inhomogeneity is modeled by cross-entropy energy functional and segmentation-oriented image decomposition. Third, the proposed model is solved as variational level set formulation and a distance regularized energy functional is also incorporated for the stability of numerical computation. The model was evaluated on a set of cardiac CTA images with comparison to related shape prior and local region-based methods and multi-atlas joint label fusion methods, and experimental results show it achieves competitive accuracies of segmenting myocardial epicardium and endocardium parts. It also presents advantage of computational burden in comparison to some popular methods such as multi-atlas joint label fusion. Since the framework of proposed method is general and adaptive, it could be potentially extended to segment similar objects in CT images.