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Cine magnetic resonance imaging (MRI) is the current gold standard for theassessment of cardiac anatomy and function. However, it typically only acquiresa set of two-dimensional (2D) slices of the underlying three-dimensional (3D)anatomy of the heart, thus limiting the understanding and analysis of bothhealthy and pathological cardiac morphology and physiology. In this paper, wepropose a novel fully automatic surface reconstruction pipeline capable ofreconstructing multi-class 3D cardiac anatomy meshes from raw cine MRIacquisitions. Its key component is a multi-class point cloud completion network(PCCN) capable of correcting both the sparsity and misalignment issues of the3D reconstruction task in a unified model. We first evaluate the PCCN on alarge synthetic dataset of biventricular anatomies and observe Chamferdistances between reconstructed and gold standard anatomies below or similar tothe underlying image resolution for multiple levels of slice misalignment.Furthermore, we find a reduction in reconstruction error compared to abenchmark 3D U-Net by 32% and 24% in terms of Hausdorff distance and meansurface distance, respectively. We then apply the PCCN as part of our automatedreconstruction pipeline to 1000 subjects from the UK Biobank study in across-domain transfer setting and demonstrate its ability to reconstructaccurate and topologically plausible biventricular heart meshes with clinicalmetrics comparable to the previous literature. Finally, we investigate therobustness of our proposed approach and observe its capacity to successfullyhandle multiple common outlier conditions.

Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images