论文标题
通过神经流量变形的无里程碑式统计形状建模
Landmark-free Statistical Shape Modeling via Neural Flow Deformations
论文作者
论文摘要
统计形状建模旨在捕获给定种群中发生的解剖结构的形状变化。形状模型用于许多任务，例如形状重建和图像分割，但也可以塑造生成和分类。现有的形状先验需要训练示例之间的密集对应，或者缺乏健壮性和拓扑保证。我们提出FlowSM，这是一种新型的形状建模方法，它可以学习形状变异性，而无需训练实例之间的密集对应关系。它依赖于连续变形流的层次结构，该层次由神经网络参数化。我们的模型的表现优于远端股骨和肝脏在提供表现力和稳健形状方面的最先进方法。我们表明，新兴的潜在表示是通过将健康与病理形状分离出来的歧视性。最终，我们从部分数据中证明了其对两个形状重建任务的有效性。我们的源代码公开可用（https://github.com/davecasp/flowssm）。
Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population. Shape models are employed in many tasks, such as shape reconstruction and image segmentation, but also shape generation and classification. Existing shape priors either require dense correspondence between training examples or lack robustness and topological guarantees. We present FlowSSM, a novel shape modeling approach that learns shape variability without requiring dense correspondence between training instances. It relies on a hierarchy of continuous deformation flows, which are parametrized by a neural network. Our model outperforms state-of-the-art methods in providing an expressive and robust shape prior for distal femur and liver. We show that the emerging latent representation is discriminative by separating healthy from pathological shapes. Ultimately, we demonstrate its effectiveness on two shape reconstruction tasks from partial data. Our source code is publicly available (https://github.com/davecasp/flowssm).
