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首页> 外文期刊>Methods of information in medicine >Automatic generation of 3D statistical shape models with optimal landmark distributions.
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Automatic generation of 3D statistical shape models with optimal landmark distributions.

机译：自动生成具有最佳地标分布的3D统计形状模型。

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OBJECTIVES: To point out the problem of non-uniform landmark placement in statistical shape modeling, to present an improved method for generating landmarks in the 3D case and to propose an unbiased evaluation metric to determine model quality. METHODS: Our approach minimizes a cost function based on the minimum description length (MDL) of the shape model to optimize landmark correspondences over the training set. In addition to the standard technique, we employ an extended remeshing method to change the landmark distribution without losing correspondences, thus ensuring a uniform distribution over all training samples. To break the dependency of the established evaluation measures generalization and specificity from the landmark distribution, we change the internal metric from landmark distance to volumetric overlap. RESULTS: Redistributing landmarks to an equally spaced distribution during the model construction phase improves the quality of the resulting models significantly if the shapes feature prominent bulges or other complex geometry. CONCLUSIONS: The distribution of landmarks on the training shapes is -- beyond the correspondence issue -- a crucial point in model construction.
机译：目的：指出统计形状建模中地标放置不均匀的问题，提出一种在3D情况下生成地标的改进方法，并提出一种无偏评估标准来确定模型质量。方法：我们的方法基于形状模型的最小描述长度（MDL）最小化成本函数，以优化训练集上的界标对应关系。除标准技术外，我们采用扩展的重新网格化方法来更改界标分布而不会丢失对应关系，从而确保所有训练样本的分布均匀。为了从地标分布中打破已建立的评估措施的一般性和特异性的依赖性，我们将内部度量标准从地标距离更改为体积重叠。结果：如果形状具有突出的凸起或其他复杂的几何形状，则在模型构建阶段将地标重新分布为等距分布，可以显着提高所得模型的质量。结论：训练形状上地标的分布-除了对应问题外-是模型构建的关键点。

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