ArticleFrontiers in cardiovascular medicine2022
Interpretable cardiac anatomy modeling using variational mesh autoencoders.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing.Computer methods in applied mechanics and engineering · 2026Article
- Unsupervised adaptive sampling graph autoencoder for 3D surface encoding and mesh representation transfer.Medical image analysis · 2026Article
- Sex-specific shape models to disentangle sex effects and age-associated anatomical variation in biventricular cardiac anatomy.European heart journal. Imaging methods and practice · 2026Article
- Neural implicit heart coordinates: 3D cardiac shape reconstruction from sparse segmentations.Medical image analysis · 2026Article
- Synthetic artificial intelligence in cardiology: from generative models to clinical applications.European heart journal open · 2026Review
- Essential Concepts in Artificial Intelligence: A Guide for Pediatric Providers.Children (Basel, Switzerland) · 2025Review
- Tracking False Lumen Remodeling with AI: A Variational Autoencoder Approach After Frozen Elephant Trunk Surgery.Journal of personalized medicine · 2025Article
- 3D cardiac shape analysis with variational point cloud autoencoders for myocardial infarction prediction and virtual heart synthesis.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025Article
- A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics.Nature machine intelligence · 2025Article
- Synthetic fibrosis distributions for data augmentation in predicting atrial fibrillation ablation outcomes: anFrontiers in cardiovascular medicine · 2025Article
- Probing the limits and capabilities of diffusion models for the anatomic editing of digital twins.NPJ digital medicine · 2024Article
- SDF4CHD: Generative modeling of cardiac anatomies with congenital heart defects.Medical image analysis · 2024Article
- Leveraging 3D Atrial Geometry for the Evaluation of Atrial Fibrillation: A Comprehensive Review.Journal of clinical medicine · 2024Review
- A review of evaluation approaches for explainable AI with applications in cardiology.Artificial intelligence review · 2024Article
- Article
- Deep Learning for Echocardiography: Introduction for Clinicians and Future Vision: State-of-the-Art Review.Life (Basel, Switzerland) · 2023Review
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14 authors.
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Abstract
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.
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