  Shape reconstruction from imaging volumes is a recurring need in medical
image analysis. Common workflows start with a segmentation step, followed by
careful post-processing and,finally, ad hoc meshing algorithms. As this
sequence can be timeconsuming, neural networks are trained to reconstruct
shapes through template deformation. These networks deliver state-ofthe-art
results without manual intervention, but, so far, they have primarily been
evaluated on anatomical shapes with little topological variety between
individuals. In contrast, other works favor learning implicit shape models,
which have multiple benefits for meshing and visualization. Our work follows
this direction by introducing deep medial voxels, a semi-implicit
representation that faithfully approximates the topological skeleton from
imaging volumes and eventually leads to shape reconstruction via convolution
surfaces. Our reconstruction technique shows potential for both visualization
and computer simulations.
