Registration-based segmentation of murine 4D cardiac micro-CT data using symmetric normalization

Micro-CT can play an important role in preclinical studies of cardiovascular disease because of its high spatial and temporal resolution. Quantitative analysis of 4D cardiac images requires segmentation of the cardiac chambers at each time point, an extremely time consuming process if done manually. To improve throughput this study proposes a pipeline for registration-based segmentation and functional analysis of 4D cardiac micro-CT data in the mouse. Following optimization and validation using simulations, the pipeline was applied to in vivo cardiac micro-CT data corresponding to ten cardiac phases acquired in C57BL/6 mice (n = 5). After edge-preserving smoothing with a novel adaptation of 4D bilateral filtration, one phase within each cardiac sequence was manually segmented. Deformable registration was used to propagate these labels to all other cardiac phases for segmentation. The volumes of each cardiac chamber were calculated and used to derive stroke volume, ejection fraction, cardiac output, and cardiac index. Dice coefficients and volume accuracies were used to compare manual segmentations of two additional phases with their corresponding propagated labels. Both measures were, on average, >0.90 for the left ventricle and >0.80 for the myocardium, the right ventricle, and the right atrium, consistent with trends in inter- and intra-segmenter variability. Segmentation of the left atrium was less reliable. On average, the functional metrics of interest were underestimated by 6.76% or more due to systematic label propagation errors around atrioventricular valves; however, execution of the pipeline was 80% faster than performing analogous manual segmentation of each phase.