Automatic Construction of Statistical Shape Models for Vertebrae
Automatic Construction of Statistical Shape Models for Vertebrae
复制标题
自动构建椎骨统计形状模型
DOI：
10.1007/978-3-642-23629-7_61
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
S. Wesarg
中科院分区：
文献类型：
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作者：
Meike Becker;M. Kirschner;Simon Fuhrmann;S. Wesarg
对于分割复杂的结构，如椎骨，先验知识的统计形状模型（SSM）的手段，往往被纳入。使用SSM的主要挑战之一是解决对应问题。在这项工作中，我们提出了一个通用的自动化方法来解决对应问题的椎骨。我们在参考形状上确定两个闭合循环，并将它们一致地传播到训练集的其余形状。然后每个形状都沿着这些循环沿着切割并参数化为矩形。在那里，我们优化了一种新的组合能量，以建立对应关系，并减少不可避免的面积和角度失真。最后，我们提出了一个自适应的rescurve方法，以实现良好的形状表示。定性和定量的评价表明，使用我们的方法，我们可以生成更高质量的SSM比ICP的方法。
For segmenting complex structures like vertebrae, a priori knowledge by means of statistical shape models (SSMs) is often incorporated. One of the main challenges using SSMs is the solution of the correspondence problem. In this work we present a generic automated approach for solving the correspondence problem for vertebrae. We determine two closed loops on a reference shape and propagate them consistently to the remaining shapes of the training set. Then every shape is cut along these loops and parameterized to a rectangle. There, we optimize a novel combined energy to establish the correspondences and to reduce the unavoidable area and angle distortion. Finally, we present an adaptive resampling method to achieve a good shape representation. A qualitative and quantitative evaluation shows that using our method we can generate SSMs of higher quality than the ICP approach.