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.

翻译：临床实践中常用的心脏功能全局单值生物标志物（如射血分数）对真实三维心脏形变过程的洞察有限，从而制约了对健康及病理性心脏力学机制的理解。本文提出点云形变网络（PCD-Net）——一种新颖的几何深度学习方法，用于建模心动周期极值点之间的3D心脏收缩与舒张过程。该方法将基于点云的深度学习前沿进展融入编码器-解码器结构，从而直接在心脏解剖结构的多类3D点云表示上实现高效的多尺度特征学习。我们基于英国生物样本库研究中超过10,000例样本的大规模数据集评估该方法，结果显示预测解剖结构与真实解剖结构之间的平均倒角距离低于原始图像采集的像素分辨率。此外，预测群体与真实群体的临床指标观测值相似，且PCD-Net能够成功捕捉正常受试者与心肌梗死（MI）患者之间的亚群特异性差异。进一步研究表明，在流行性MI检测与偶发性MI预测任务中，所学习的3D形变模式在受试者工作特征曲线下面积上分别超越多个临床基准模型13%和7%；在MI生存分析中，其Harrell一致性指数提升7%。