  The efficient construction of anatomical models is one of the major
challenges of patient-specific in-silico models of the human heart. Current
methods frequently rely on linear statistical models, allowing no advanced
topological changes, or requiring medical image segmentation followed by a
meshing pipeline, which strongly depends on image resolution, quality, and
modality. These approaches are therefore limited in their transferability to
other imaging domains. In this work, the cardiac shape is reconstructed by
means of three-dimensional deep signed distance functions with Lipschitz
regularity. For this purpose, the shapes of cardiac MRI reconstructions are
learned to model the spatial relation of multiple chambers. We demonstrate that
this approach is also capable of reconstructing anatomical models from partial
data, such as point clouds from a single ventricle, or modalities different
from the trained MRI, such as the electroanatomical mapping (EAM).
