用优化方法改进解剖形状建模，更精准控制形态特征。
Optimization-Driven Statistical Models of Anatomies using Radial Basis Function Shape Representation
- 结合主成分形状与对应损失，通过优化生成精确粒子分布
- 在两个真实数据集上优于现有方法，显著提升模型精度
- 避免黑箱模型，适合需要可解释性的医学影像研究
基于粒子的形状建模（PSM）是自动量化解剖群体形态变异的常用方法。该类方法通过优化在三维表面上自动生成密集的对应粒子（伪地标），以支持后续形状分析。最近的深度学习方法利用隐式径向基函数表示形状，更好地适应解剖结构的复杂几何特性。本文提出一种基于传统优化的改进方法，通过引入主成分形状和对应损失，实现对模型特性的更精确控制。该方法避免使用黑箱模型，使粒子能更自由地在表面移动，从而生成更具信息量的统计模型。我们在两个真实数据集上验证了该方法的有效性，并通过实证分析证明了损失函数选择的合理性。
原文摘要 · Abstract (English)
Particle-based shape modeling (PSM) is a popular approach to automatically quantify shape variability in populations of anatomies. The PSM family of methods employs optimization to automatically populate a dense set of corresponding particles (as pseudo landmarks) on 3D surfaces to allow subsequent shape analysis. A recent deep learning approach leverages implicit radial basis function representations of shapes to better adapt to the underlying complex geometry of anatomies. Here, we propose an adaptation of this method using a traditional optimization approach that allows more precise control over the desired characteristics of models by leveraging both an eigenshape and a correspondence loss. Furthermore, the proposed approach avoids using a black-box model and allows more freedom for particles to navigate the underlying surfaces, yielding more informative statistical models. We demonstrate the efficacy of the proposed approach to state-of-the-art methods on two real datasets and justify our choice of losses empirically.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。