Kohlberger Timo, Cremers Daniel, Rousson Mikaël, Ramaraj Ramamani, Funka-Lea Gareth
Siemens Corporate Research, Inc., Imaging and Visualization Department, Princeton, NJ, USA.
Med Image Comput Comput Assist Interv. 2006;9(Pt 1):92-100. doi: 10.1007/11866565_12.
We develop a 4D (3D plus time) statistical shape model for implicit level set based shape representations. To this end, we represent hand segmented training sequences of the left ventricle by respective 4-dimensional embedding functions and approximate these by a principal component analysis. In contrast to recent 4D models on explicit shape representations, the implicit shape model developed in this work does not require the computation of point correspondences which is known to be quite challenging, especially in higher dimensions. Experimental results on the segmentation of SPECT sequences of the left myocardium confirm that the 4D shape model outperforms respective 3D models, because it takes into account a statistical model of the temporal shape evolution.
我们为基于隐式水平集的形状表示开发了一种4D（3D加时间）统计形状模型。为此，我们通过各自的4维嵌入函数来表示左心室的手动分割训练序列，并通过主成分分析对这些函数进行近似。与最近关于显式形状表示的4D模型不同，本文中开发的隐式形状模型不需要计算点对应关系，而点对应关系的计算已知具有相当大的挑战性，尤其是在高维情况下。左心肌SPECT序列分割的实验结果证实，4D形状模型优于相应的3D模型，因为它考虑了时间形状演变的统计模型。
