Left ventricle endocardium segmentation for cardiac CT volumes using an optimal smooth surface
Yefeng Zheng, Bogdan Georgescu, Fernando Vega-Higuera, Dorin Comaniciu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
We recently proposed a robust heart chamber segmentation approach based on marginal space learning. In this paper, we focus on improving the LV endocardium segmentation accuracy by searching for an optimal smooth mesh that tightly encloses the whole blood pool. The refinement procedure is formulated as an optimization problem: maximizing the surface smoothness under the tightness constraint. The formulation is a convex quadratic programming problem, therefore has a unique global optimum and can be solved efficiently. Our approach has been validated on the largest cardiac CT dataset (457 volumes from 186 patients) ever reported. Compared to our previous work, it reduces the mean point-to-mesh error from 1.13 mm to 0.84 mm (22% improvement). Additionally, the system has been extensively tested on a dataset with 2000+ volumes without any major failure.