  Designing of a cranial implant needs a 3D understanding of the complete skull
shape. Thus, taking a 2D approach is sub-optimal, since a 2D model lacks a
holistic 3D view of both the defective and healthy skulls. Further, loading the
whole 3D skull shapes at its original image resolution is not feasible in
commonly available GPUs. To mitigate these issues, we propose a fully
convolutional network composed of two subnetworks. The first subnetwork is
designed to complete the shape of the downsampled defective skull. The second
subnetwork upsamples the reconstructed shape slice-wise. We train the 3D and 2D
networks together end-to-end, with a hierarchical loss function. Our proposed
solution accurately predicts a high-resolution 3D implant in the challenge test
case in terms of dice-score and the Hausdorff distance.
