Anatomy shape modeling is a fundamental problem in medical data analysis. However, the geometric complexity and topological variability of anatomical structures pose significant challenges to accurate anatomical shape generation. In this work, we propose a skeletal latent diffusion framework that explicitly incorporates structural priors for efficient and high-fidelity medical shape generation. We introduce a shape auto-encoder in which the encoder captures global geometric information through a differentiable skeletonization module and aggregates local surface features into shape latents, while the decoder predicts the corresponding implicit fields over sparsely sampled coordinates. New shapes are generated via a latent-space diffusion model, followed by neural implicit decoding and mesh extraction. To address the limited availability of medical shape data, we construct a large-scale dataset, \textit{MedSDF}, comprising surface point clouds and corresponding signed distance fields across multiple anatomical categories. Extensive experiments on MedSDF and vessel datasets demonstrate that the proposed method achieves superior reconstruction and generation quality while maintaining a higher computational efficiency compared with existing approaches. Code is available at: https://github.com/wlsdzyzl/meshage.

翻译：解剖形状建模是医学数据分析中的一个基础性问题。然而，解剖结构的几何复杂性和拓扑可变性给精确的解剖形状生成带来了重大挑战。在本工作中，我们提出了一种骨骼潜在扩散框架，该框架显式地结合了结构先验，以实现高效且高保真的医学形状生成。我们引入了一种形状自动编码器，其中编码器通过一个可微骨架化模块捕获全局几何信息，并将局部表面特征聚合为形状潜在表示；而解码器则在稀疏采样的坐标上预测相应的隐式场。新形状的生成通过一个潜在空间扩散模型实现，随后进行神经隐式解码和网格提取。针对医学形状数据可用性有限的问题，我们构建了一个大规模数据集 \textit{MedSDF}，包含多个解剖类别的表面点云及其对应的有符号距离场。在 MedSDF 和血管数据集上进行的大量实验表明，与现有方法相比，所提出的方法在保持更高计算效率的同时，实现了卓越的重建和生成质量。代码发布于：https://github.com/wlsdzyzl/meshage。