Heart Modeling by Convexity Preserving Segmentation and Convex Shape Decomposition

摘要

This paper proposes a convexity-preserving level set (CPLS) and a novel modeling of the heart named Convex Shape Decomposition (CSD) for segmentation of Left Ventricle (LV) and Right Ventricle (RV) from cardiac magnetic resonance images. The main contributions are two-fold. First, we introduce a convexity preserving mechanism in the level set framework, which is helpful for overcoming the difficulties arised from the overlap between intensities of papillary muscles and tra-beculae and intensities of myocardium. Furthermore, such a generally contrained convexity-preserving level set method can be useful in many other potential applications. Second, by decomposing the heart into two convex structures, and essentially converting RV segmentation into LV segmentation, we can solve both LV and RV segmentation in a unified framework without training any specific shape models for RV. The proposed method has been quantitatively validated on open datasets, and the experimental results and comparisons with other methods demonstrate the superior performance of our method.