  Global single-valued biomarkers of cardiac function typically used in
clinical practice, such as ejection fraction, provide limited insight on the
true 3D cardiac deformation process and hence, limit the understanding of both
healthy and pathological cardiac mechanics. In this work, we propose the Point
Cloud Deformation Network (PCD-Net) as a novel geometric deep learning approach
to model 3D cardiac contraction and relaxation between the extreme ends of the
cardiac cycle. It employs the recent advances in point cloud-based deep
learning into an encoder-decoder structure, in order to enable efficient
multi-scale feature learning directly on multi-class 3D point cloud
representations of the cardiac anatomy. We evaluate our approach on a large
dataset of over 10,000 cases from the UK Biobank study and find average Chamfer
distances between the predicted and ground truth anatomies below the pixel
resolution of the underlying image acquisition. Furthermore, we observe similar
clinical metrics between predicted and ground truth populations and show that
the PCD-Net can successfully capture subpopulation-specific differences between
normal subjects and myocardial infarction (MI) patients. We then demonstrate
that the learned 3D deformation patterns outperform multiple clinical
benchmarks by 13% and 7% in terms of area under the receiver operating
characteristic curve for the tasks of prevalent MI detection and incident MI
prediction and by 7% in terms of Harrell's concordance index for MI survival
analysis.
