Integrated 3D anatomical model for automatic myocardial segmentation in cardiac CT imagery

摘要

Segmentation of epicardial and endocardial boundaries is a critical step in diagnosing cardiovascular function in heart patients. The manual tracing of organ contours in computed tomography angiography (CTA) slices is subjective, time-consuming and impractical in clinical setting. We propose a novel multidimensional automatic edge detection algorithm based on shape priors and principal component analysis (PCA). We have developed a highly customised parametric model for implicit representations of segmenting curves (3D) for left ventricle (LV), right ventricle (RV) and epicardium (Epi) used simultaneously to achieve myocardial segmentation. We have combined these representations in a region-based image modelling framework with high-level constraints enabling the modelling of complex cardiac anatomical structures to automatically guide the segmentation of endo/epicardial boundaries. Test results on 30 short-axis CTA datasets show robust segmentation with error (mean ± std mm) of (1.46 ± 0.41), (2.06 ± 0.65) and (2.88 ± 0.59) for LV, RV and Epi, respectively.