让扩散模型精准控制人体结构的形状与连接关系。
Anatomica: Localized Control over Geometric and Topological Properties for Anatomical Diffusion Models
- 用可变立方体区域局部操控解剖结构生成
- 通过拓扑不变量和体素矩精确控制几何与连通性
- 适用于多类器官建模，适合虚拟实验数据设计
我们提出 Anatomica：一种生成多类别解剖体素图的推理时框架，支持局部几何与拓扑控制。生成过程中，采用不同维度、位置与形状的立方体控制域切割出相关子结构，通过可微惩罚函数引导样本满足目标约束。利用体素级矩控制尺寸、形状与位置等几何特征，借助持久同调（persistent homology）强制实现连通分量、环路与空洞等拓扑特性。该方法应用于潜在扩散模型，由神经场解码器部分提取子结构，实现高效解剖属性调控。Anatomica 可灵活适配多种解剖系统，在任意维度与坐标系下组合约束，实现复杂结构的理性合成，适用于虚拟临床试验或机器学习工作流中的合成数据生成。
原文摘要 · Abstract (English)
We present Anatomica: an inference-time framework for generating multi-class anatomical voxel maps with localized geo-topological control. During generation, we use cuboidal control domains of varying dimensionality, location, and shape to slice out relevant substructures. These local substructures are used to compute differentiable penalty functions that steer the sample towards target constraints. We control geometric features such as size, shape, and position through voxel-wise moments, while topological features such as connected components, loops, and voids are enforced through persistent homology. Lastly, we implement Anatomica for latent diffusion models, where neural field decoders partially extract substructures, enabling the efficient control of anatomical properties. Anatomica applies flexibly across diverse anatomical systems, composing constraints to control complex structures over arbitrary dimensions and coordinate systems, thereby enabling the rational design of synthetic datasets for virtual trials or machine learning workflows.
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