让解剖表面的粒子模型自动适应局部几何特征，提升配准精度
Adaptive Particle-Based Shape Modeling for Anatomical Surface Correspondence
- 引入邻域对应损失，使粒子配置自适应局部几何
- 提出测地线对应算法，保持邻域一致性，提升优化稳定性
- 在复杂解剖数据上验证了自适应与对应关系的平衡性
基于粒子的形状建模（PSM）通过在解剖结构表面放置一致配置的粒子（伪地标）来自动量化群体间的形状变异。近期方法利用隐式径向基函数表示作为自监督信号，更好地捕捉解剖结构的复杂几何特性。然而，这些方法仍缺乏自适应能力——即无法根据每个表面的局部几何特征自动调整粒子配置，而这对于准确表示复杂解剖变异至关重要。本文提出两种机制以增强表面自适应性，同时保持粒子配置的一致性：(1) 一种新颖的邻域对应损失，实现高自适应性；(2) 一种测地线对应算法，通过正则化优化过程，强制保持测地线邻域一致性。我们在具有挑战性的数据集上评估了该方法的有效性与可扩展性，详细分析了自适应性与对应关系之间的权衡，并在表面表示精度和对应度量上对现有方法进行了基准测试。
原文摘要 · Abstract (English)
Particle-based shape modeling (PSM) is a family of approaches that automatically quantifies shape variability across anatomical cohorts by positioning particles (pseudo landmarks) on shape surfaces in a consistent configuration. Recent advances incorporate implicit radial basis function representations as self-supervised signals to better capture the complex geometric properties of anatomical structures. However, these methods still lack self-adaptivity -- that is, the ability to automatically adjust particle configurations to local geometric features of each surface, which is essential for accurately representing complex anatomical variability. This paper introduces two mechanisms to increase surface adaptivity while maintaining consistent particle configurations: (1) a novel neighborhood correspondence loss to enable high adaptivity and (2) a geodesic correspondence algorithm that regularizes optimization to enforce geodesic neighborhood consistency. We evaluate the efficacy and scalability of our approach on challenging datasets, providing a detailed analysis of the adaptivity-correspondence trade-off and benchmarking against existing methods on surface representation accuracy and correspondence metrics.
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