Left ventricle segmentation using data-driven priors and temporal correlations

Abstract

Cardiac MRI has been widely used in the study of heart diseases and transplant rejections using small animal models. However, due to low image quality, quantitative analysis of the MRI data has to be performed through tedious manual segmentation. In this thesis, a novel approach based on data-driven priors and temporal correlations is proposed for the segmentation of left ventricle myocardium in cardiac MR images of native and transplanted rat hearts. To incorporate data-driven 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 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. Left ventricle endocardium and epicardium segmentation results obtained by the proposed method respectively achieve 87.1 ± 2.61% and 87.79 ± 3.51% average area similarity and 83.16 ± 8.14% and 91.19 ± 2.78% average shape similarity with respect to manual segmentations done by experts. With minimal user input, myocardium contours obtained by the proposed method exhibit excellent agreement with the gold standard and good temporal consistency. More importantly, it avoids inter- and intra- observer variations and makes accurate quantitative analysis of low-quality cardiac MR images possible.