Model-based 4D segmentation of cardiac structures in cine MRI sequences

Abstract

A temporally consistent segmentation of cardiac structures in spatio-temporal cine MRI sequences is a prerequisite for in-depth analyses of the heart dynamics in clinical practice. Despite its great importance, automated cardiac segmentation is still an open problem, especially for spatio-temporal data due to challenging imaging characteristics, large anatomical heart variability, and diversity of cardiac dynamics. To cope with these challenges, an approach for model-based 4D segmentation of the left and right ventricle in clinical cine MRI sequences is presented in this paper. Central to our approach is a 4D statistical shape model that accounts for both inter- and intra-patient variability. It is fitted to the spatio-temporal image sequence by applying a computationally efficient MRF-based discrete optimization approach that uses BRIEF descriptors for image matching. The approach is evaluated on 15 cardiac cine MRI sequences of children and adults with different heart abnormalities. The segmentation results are compared with another effective 4D segmentation technique indicating similar segmentation accuracy but improved coherence and runtime performances.