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Interpretable cardiac anatomy modeling using variational mesh autoencoders
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 c...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9813669/ https://www.ncbi.nlm.nih.gov/pubmed/36620629 http://dx.doi.org/10.3389/fcvm.2022.983868 |
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author | 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 |
author_facet | 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 |
author_sort | Beetz, Marcel |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9813669 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98136692023-01-06 Interpretable cardiac anatomy modeling using variational mesh autoencoders 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 Front Cardiovasc Med 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. Frontiers Media S.A. 2022-12-22 /pmc/articles/PMC9813669/ /pubmed/36620629 http://dx.doi.org/10.3389/fcvm.2022.983868 Text en Copyright © 2022 Beetz, Corral Acero, Banerjee, Eitel, Zacur, Lange, Stiermaier, Evertz, Backhaus, Thiele, Bueno-Orovio, Lamata, Schuster and Grau. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cardiovascular Medicine 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 Interpretable cardiac anatomy modeling using variational mesh autoencoders |
title | Interpretable cardiac anatomy modeling using variational mesh autoencoders |
title_full | Interpretable cardiac anatomy modeling using variational mesh autoencoders |
title_fullStr | Interpretable cardiac anatomy modeling using variational mesh autoencoders |
title_full_unstemmed | Interpretable cardiac anatomy modeling using variational mesh autoencoders |
title_short | Interpretable cardiac anatomy modeling using variational mesh autoencoders |
title_sort | interpretable cardiac anatomy modeling using variational mesh autoencoders |
topic | Cardiovascular Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9813669/ https://www.ncbi.nlm.nih.gov/pubmed/36620629 http://dx.doi.org/10.3389/fcvm.2022.983868 |
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