Myocardial infarction (MI) is one of the most prevalent cardiovascular
diseases with associated clinical decision-making typically based on
single-valued imaging biomarkers. However, such metrics only approximate the
complex 3D structure and physiology of the heart and hence hinder a better
understanding and prediction of MI outcomes. In this work, we investigate the
utility of complete 3D cardiac shapes in the form of point clouds for an
improved detection of MI events. To this end, we propose a fully automatic
multi-step pipeline consisting of a 3D cardiac surface reconstruction step
followed by a point cloud classification network. Our method utilizes recent
advances in geometric deep learning on point clouds to enable direct and
efficient multi-scale learning on high-resolution surface models of the cardiac
anatomy. We evaluate our approach on 1068 UK Biobank subjects for the tasks of
prevalent MI detection and incident MI prediction and find improvements of ~13%
and ~5% respectively over clinical benchmarks. Furthermore, we analyze the role
of each ventricle and cardiac phase for 3D shape-based MI detection and conduct
a visual analysis of the morphological and physiological patterns typically
associated with MI outcomes.

本文中，我们提出了一种利用完整的三维心脏形状的点云提高心肌梗死事件检测的方法，并通过对 1068 名英国生物银行受试者进行评估，在流行性心肌梗死检测和新发性心肌梗死预测任务上实现了分别超过临床基准约 13％和 5％的进展。此外，我们分析了每个心室和心脏时相在基于三维形状的心肌梗死检测中的作用，并进行了形态和生理模式的视觉分析。