Rapid inference of personalised left-ventricular meshes by deformation-based differentiable mesh voxelization

We propose a differentiable volumetric mesh voxelization technique based on deformation of a shape-model, and demonstrate that it can be used to predict left-ventricular anatomies directly from magnetic resonance image slice data. The predicted anatomies are volumetric meshes suitable for direct inclusion in biophysical simulations. The proposed method can leverage existing (pixel-based) segmentation networks, and does not require any ground truth paired image and mesh training data. We demonstrate that this approach produces accurate predictions from few slices, and can combine information from images acquired in different views (e.g. fusing shape information from short axis and long axis slices). We demonstrate that the proposed method is several times faster than a state-of-the-art registration based method. Additionally, we show that our method can correct for slice misalignment, and is robust to incomplete and inaccurate input data. We further demonstrate that by fitting a mesh to every frame of 4D data we can determine ejection fraction, stroke volume and strain.