3D Shape-Based Myocardial Infarction Prediction Using Point Cloud
  Classification Networks
Marcel Beetz Yilong Yang Abhirup Banerjee Lei Li Vicente Grau
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.