Keywords: Attention, Generative modelling, Geometric Deep Learning, Graph Neural Networks, Shape matching, Virtual populations
Abstract: Generative statistical models have a wide range of applications in the modelling of anatomies. In-silico clinical trials of medical devices, for instance, require the development of virtual populations of anatomy that capture enough variability while remaining plausible. Model construction and use are heavily influenced by the correspondence problem and establishing shape matching over a large number of training data.This study focuses on generating virtual cohorts of left ventricle geometries resembling different-sized shape populations, suitable for in-silico experiments. We present an unsupervised data-driven probabilistic generative model for shapes. This framework incorporates an attention-based shape matching procedure using graph neural networks, coupled with a $\beta-$VAE generation model, eliminating the need for initial shape correspondence. Left ventricle shapes derived from cardiac magnetic resonance images available in the UK Biobank are utilized for training and validating the framework. We investigate our method’s generative capabilities in terms of generalisation and specificity and show that it is able to synthesise virtual populations of realistic shapes with volumetric measurements in line with actual clinical indices. Moreover, results show our method outperforms joint registration-PCA-based models.