In Frontiers in cardiovascular medicine
Cardiac anatomy and function vary considerably across the human population with important implications for clinical diagnosis and treatment planning. Consequently, many computer-based approaches have been developed to capture this variability for a wide range of applications, including explainable cardiac disease detection and prediction, dimensionality reduction, cardiac shape analysis, and the generation of virtual heart populations. In this work, we propose a variational mesh autoencoder (mesh VAE) as a novel geometric deep learning approach to model such population-wide variations in cardiac shapes. It embeds multi-scale graph convolutions and mesh pooling layers in a hierarchical VAE framework to enable direct processing of surface mesh representations of the cardiac anatomy in an efficient manner. The proposed mesh VAE achieves low reconstruction errors on a dataset of 3D cardiac meshes from over 1,000 patients with acute myocardial infarction, with mean surface distances between input and reconstructed meshes below the underlying image resolution. We also find that it outperforms a voxelgrid-based deep learning benchmark in terms of both mean surface distance and Hausdorff distance while requiring considerably less memory. Furthermore, we explore the quality and interpretability of the mesh VAE's latent space and showcase its ability to improve the prediction of major adverse cardiac events over a clinical benchmark. Finally, we investigate the method's ability to generate realistic virtual populations of cardiac anatomies and find good alignment between the synthesized and gold standard mesh populations in terms of multiple clinical metrics.
Beetz Marcel, Corral Acero Jorge, Banerjee Abhirup, Eitel Ingo, Zacur Ernesto, Lange Torben, Stiermaier Thomas, Evertz Ruben, Backhaus Sören J, Thiele Holger, Bueno-Orovio Alfonso, Lamata Pablo, Schuster Andreas, Grau Vicente
2022
3D ventricular shape analysis, acute myocardial infarction, clinical outcome prediction, geometric deep learning, graph neural networks, major adverse cardiac events, mesh VAE, virtual anatomy generation
