Segmentation of cardiac MRI in a heart transplant study using rodent models

Abstract

Cardiac MRI has been widely used in the study of heart diseases and transplant rejections using small animal models. But due to low image quality, quantitative analysis of the MRI data is generally performed through tedious manual segmentation. In this paper, a novel approach based on datadriven priors and temporal correlations is proposed for the segmentation of left ventricle myocardium in cardiac MR images of both native and transplanted rat hearts. To incorporate datadriven constraints into the segmentation, probabilistic maps generated based on prominent image features, i.e., corner points and scale-invariant edges, are used as priors for endocardium and epicardium segmentation, respectively. Non-rigid image registration is performed to obtain the deformation fields, which are then used to compute the averaged probabilistic priors and feature spaces. Integrating data-driven priors and temporal correlations with intensity, texture, and edge information, a level set formulation is adopted for segmentation. The proposed algorithm was applied to 3D+t cardiac MR images from eight rat studies. Experimental results show that with minimal user input, representative priors are correctly extracted from the data itself, and the proposed method is effective and robust for the segmentation of the left ventricle myocardium even in images with very low contrast. More importantly, it avoids interand intra- observer variations and makes accurate quantitative analysis of low-quality cardiac MR images possible.