Generative modelling for shapes is a prerequisite for In-Silico Clinical
Trials (ISCTs), which aim to cost-effectively validate medical device
interventions using synthetic anatomical shapes, often represented as 3D
surface meshes. However, constructing AI models to generate shapes closely
resembling the real mesh samples is challenging due to variable vertex counts,
connectivities, and the lack of dense vertex-wise correspondences across the
training data. Employing graph representations for meshes, we develop a novel
unsupervised geometric deep-learning model to establish refinable shape
correspondences in a latent space, construct a population-derived atlas and
generate realistic synthetic shapes. We additionally extend our proposed base
model to a joint shape generative-clustering multi-atlas framework to
incorporate further variability and preserve more details in the generated
shapes. Experimental results using liver and left-ventricular models
demonstrate the approach's applicability to computational medicine,
highlighting its suitability for ISCTs through a comparative analysis.

基于几何深度学习的形状生成模型在计算医学中的应用可通过比较分析，验证了其适用于以真实 3D 网格形状为基础的合成解剖形状，并且可以进一步增加变异性并保留更多生成形状的细节。