  Automated construction of surface geometries of cardiac structures from
volumetric medical images is important for a number of clinical applications.
While deep-learning based approaches have demonstrated promising reconstruction
precision, these approaches have mostly focused on voxel-wise segmentation
followed by surface reconstruction and post-processing techniques. However,
such approaches suffer from a number of limitations including disconnected
regions or incorrect surface topology due to erroneous segmentation and
stair-case artifacts due to limited segmentation resolution. We propose a novel
deep-learning-based approach that directly predicts whole heart surface meshes
from volumetric CT and MR image data. Our approach leverages a graph
convolutional neural network to predict deformation on mesh vertices from a
pre-defined mesh template to reconstruct multiple anatomical structures in a 3D
image volume. Our method demonstrated promising performance of generating
high-resolution and high-quality whole heart reconstructions and outperformed
prior deep-learning based methods on both CT and MR data in terms of precision
and surface quality. Furthermore, our method can more efficiently produce
temporally-consistent and feature-corresponding surface mesh predictions for
heart motion from CT or MR cine sequences, and therefore can potentially be
applied for efficiently constructing 4D whole heart dynamics.
