Post-Infarction Risk Prediction with Mesh Classification Networks

Marcel Beetz*, Jorge Corral Acero, Abhirup Banerjee, Ingo Eitel, Ernesto Zacur, Torben Lange, Thomas Stiermaier, Ruben Evertz, Sören J. Backhaus, Holger Thiele, Alfonso Bueno-Orovio, Pablo Lamata, Andreas Schuster, Vicente Grau

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Post-myocardial infarction (MI) patients are at risk of major adverse cardiac events (MACE), with risk stratification primarily based on global image-based biomarkers, such as ejection fraction, in current clinical practice. However, these metrics neglect more subtle and localized shape differences in 3D cardiac anatomy and function, which limit predictive accuracy. In this work, we propose a novel geometric deep learning approach to directly predict MACE outcomes within 1 year after the infarction event from high-resolution 3D cardiac anatomy meshes. Its architecture is specifically designed for direct and efficient processing of surface mesh data with a hierarchical, multi-scale structure to enable both local and global feature learning. We evaluate the binary MACE prediction capabilities of the proposed mesh classification network on a multi-center dataset of post-MI patients. Our results show that the proposed method outperforms corresponding clinical benchmarks by ∼ 16% and ∼ 6% in terms of area under the receiver operating characteristic (AUROC) curve for 3D shape and 3D contraction inputs, respectively. Furthermore, we visually analyze both 3D cardiac shapes and 3D contraction patterns with regards to their MACE predictability and demonstrate how task-specific information learned by the network on a balanced dataset successfully generalizes to increasing levels of class imbalance. Finally, we compare our approach to both clinical and machine learning benchmarks on our original highly-imbalanced dataset of post-MI patients and find average improvements in AUROC scores of ∼ 9% and ∼ 3%, respectively.

Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers - 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Revised Selected Papers
EditorsOscar Camara, Esther Puyol-Antón, Avan Suinesiaputra, Alistair Young, Chen Qin, Maxime Sermesant, Shuo Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages291-301
Number of pages11
ISBN (Print)9783031234422
DOIs
Publication statusPublished - 2022
Event13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: 18 Sept 202218 Sept 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13593 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period18/09/202218/09/2022

Keywords

  • 3D cardiac contraction
  • 3D cardiac shape
  • Cardiac MRI
  • Geometric deep learning
  • Graph neural networks
  • Major adverse cardiac event prediction
  • Mesh pooling
  • Post-myocardial infarction

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