Seshamani Sharmishtaa, Chintalapani Gouthami, Taylor Russell
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):417-25. doi: 10.1007/978-3-642-23629-7_51.
Statistical atlases of bone anatomy are traditionally constructed with point-based models. These methods establish initial point correspondences across the population of shapes and model variations in the shapes using a variety of statistical tools. A drawbacks of such methods is that initial point correspondences are not updated after their first establishment. This paper proposes an iterative method for refining point correspondences for statistical atlases. The statistical model is used to estimate the direction of "pull" along the surface and consistency checks are used to ensure that illegal shapes are not generated. Our method is much faster that previous methods since it does not rely on computationally expensive deformable registration. It is also generalizable and can be used with any statististical model. We perform experiments on a human pelvis atlas consisting of 110 healthy patients and demonstrate that the method can be used to re-estimate point correspondences which reduce the hausdorff distance from 3.2mm to 2.7mm and the surface error from 1.6mm to 1.4mm for PCA modelling with 20 modes.
传统上，骨骼解剖结构的统计图谱是基于点模型构建的。这些方法在形状总体上建立初始点对应关系，并使用各种统计工具对形状变化进行建模。此类方法的一个缺点是，初始点对应关系在首次建立后不会更新。本文提出了一种用于细化统计图谱点对应关系的迭代方法。统计模型用于估计沿表面的“拉动”方向，并使用一致性检查来确保不会生成非法形状。我们的方法比以前的方法快得多，因为它不依赖于计算成本高昂的可变形配准。它还具有通用性，可与任何统计模型一起使用。我们对由110名健康患者组成的人体骨盆图谱进行了实验，并证明该方法可用于重新估计点对应关系，对于20种模式的主成分分析建模，这将豪斯多夫距离从3.2毫米减少到2.7毫米，表面误差从1.6毫米减少到1.4毫米。
