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.

我们提出了点云变形网络（PCD-Net）作为一种新颖的几何深度学习方法，用于模拟心脏循环极端端点之间的三维心脏收缩和舒张过程。我们对来自英国生物库研究（UK Biobank study）中超过 10,000 例数据集进行评估，发现预测值与实际解剖结构之间的平均 Chamfer 距离小于图像获取的像素分辨率。此外，我们观察到预测值与实际群体之间的类似临床指标，并且证明了 PCD-Net 能够成功捕捉正常受试者和心肌梗死（MI）患者之间的亚人群特异性差异。然后，我们证明了学习到的三维变形模式在预测患有 MI 的面前 MI 检测和潜在 MI 预测任务方面优于多个临床指标，对于 MI 生存分析的 Harrell's concordance 指数提高了 7%。