Generating Subpopulation-Specific Biventricular Anatomy Models Using Conditional Point Cloud Variational Autoencoders

Abstract

Generative statistical models have a wide variety of applications in modelling of cardiac anatomy and function, including disease diagnosis and prediction, personalized shape analysis, and generation of population cohorts for electrophysiological and mechanical computer simulations. In this work, we propose a novel geometric deep learning method based on the variational autoencoder (VAE) framework capable of accurately encoding, reconstructing, and synthesizing 3D surface models of the biventricular anatomy. Our non-linear approach works directly with memory-efficient point clouds and is able to process multiple substructures of the cardiac anatomy at the same time in a multi-class setting. Furthermore, we introduce subpopulation-specific characteristics as additional conditional inputs to allow the generation of new personalized anatomies. Our method achieves high reconstruction quality on a dataset derived from the UK Biobank study with average Chamfer distances between reconstructed and gold standard point clouds below the underlying image pixel resolution, for all anatomical substructures and combinations of conditional inputs. We investigate our method’s generative capabilities and show that it is able to synthesize virtual populations of realistic hearts with volumetric measurements in line with established clinical precedent. We also analyse the effects of variations in the latent space of the autoencoder on the generated anatomies and find interpretable changes in cardiac shapes and sizes.