  Cardiovascular magnetic resonance imaging is emerging as a crucial tool to
examine cardiac morphology and function. Essential to this endeavour are
anatomical 3D surface and volumetric meshes derived from CMR images, which
facilitate computational anatomy studies, biomarker discovery, and in-silico
simulations. However, conventional surface mesh generation methods, such as
active shape models and multi-atlas segmentation, are highly time-consuming and
require complex processing pipelines to generate simulation-ready 3D meshes. In
response, we introduce HybridVNet, a novel architecture for direct
image-to-mesh extraction seamlessly integrating standard convolutional neural
networks with graph convolutions, which we prove can efficiently handle surface
and volumetric meshes by encoding them as graph structures. To further enhance
accuracy, we propose a multiview HybridVNet architecture which processes both
long axis and short axis CMR, showing that it can increase the performance of
cardiac MR mesh generation. Our model combines traditional convolutional
networks with variational graph generative models, deep supervision and
mesh-specific regularisation. Experiments on a comprehensive dataset from the
UK Biobank confirm the potential of HybridVNet to significantly advance cardiac
imaging and computational cardiology by efficiently generating high-fidelity
and simulation ready meshes from CMR images.
