An End-to-End Deep Learning Generative Framework for Refinable Shape
  Matching and Generation
Soodeh Kalaie Andy Bulpitt Alejandro F. Frangi Ali Gooya
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.