Cine magnetic resonance imaging (MRI) is the current gold standard for the
assessment of cardiac anatomy and function. However, it typically only acquires
a set of two-dimensional (2D) slices of the underlying three-dimensional (3D)
anatomy of the heart, thus limiting the understanding and analysis of both
healthy and pathological cardiac morphology and physiology. In this paper, we
propose a novel fully automatic surface reconstruction pipeline capable of
reconstructing multi-class 3D cardiac anatomy meshes from raw cine MRI
acquisitions. Its key component is a multi-class point cloud completion network
(PCCN) capable of correcting both the sparsity and misalignment issues of the
3D reconstruction task in a unified model. We first evaluate the PCCN on a
large synthetic dataset of biventricular anatomies and observe Chamfer
distances between reconstructed and gold standard anatomies below or similar to
the underlying image resolution for multiple levels of slice misalignment.
Furthermore, we find a reduction in reconstruction error compared to a
benchmark 3D U-Net by 32% and 24% in terms of Hausdorff distance and mean
surface distance, respectively. We then apply the PCCN as part of our automated
reconstruction pipeline to 1000 subjects from the UK Biobank study in a
cross-domain transfer setting and demonstrate its ability to reconstruct
accurate and topologically plausible biventricular heart meshes with clinical
metrics comparable to the previous literature. Finally, we investigate the
robustness of our proposed approach and observe its capacity to successfully
handle multiple common outlier conditions.

该研究提出了一种全新的完全自动化表面重建管道，能够从原始的 Cine 磁共振成像获取多类三维心脏解剖网格，其中关键组件是一种多类点云完成网络（PCCN）；研究人员证明了该算法在合成数据集和实际数据集中的高精度和鲁棒性。