Particle-Based Shape Modeling for Arbitrary Regions-of-Interest.
Particle-Based Shape Modeling for Arbitrary Regions-of-Interest.
复制标题
适用于任意感兴趣区域的基于粒子的形状建模。
DOI：
10.1007/978-3-031-46914-5_4
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Elhabian,ShireenY
中科院分区：
文献类型：
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作者：
Xu,Hong;Morris,Alan;Elhabian,ShireenY
统计形状建模(SSM)是一种分析解剖结构形态变化的定量方法。这些分析通常需要在感兴趣的目标解剖区域上建立模型，以专注于特定的形态特征。我们提出了一个扩展的基于粒子的形状建模(PSM)，这是一个广泛使用的SSM框架，允许对任意感兴趣的区域进行形状建模。现有的定义感兴趣区域的方法计算昂贵且具有拓扑限制。为了解决这些缺点，我们使用网格场来定义自由形式的约束，它允许在形状表面上划分任意感兴趣的区域。此外，我们在模型优化中加入了二次惩罚方法，以实现对切割平面和自由形式约束的任意组合的计算效率的强制执行。我们在一个具有挑战性的合成数据集和两个医学数据集上演示了该方法的有效性。
Statistical Shape Modeling (SSM) is a quantitative method for analyzing morphological variations in anatomical structures. These analyses often necessitate building models on targeted anatomical regions of interest to focus on specific morphological features. We propose an extension to particle-based shape modeling (PSM), a widely used SSM framework, to allow shape modeling to arbitrary regions of interest. Existing methods to define regions of interest are computationally expensive and have topological limitations. To address these shortcomings, we use mesh fields to define free-form constraints, which allow for delimiting arbitrary regions of interest on shape surfaces. Furthermore, we add a quadratic penalty method to the model optimization to enable computationally efficient enforcement of any combination of cutting-plane and free-form constraints. We demonstrate the effectiveness of this method on a challenging synthetic dataset and two medical datasets.