  In this study, we present a baseline approach for AutoImplant
(https://autoimplant.grand-challenge.org/) - the cranial implant design
challenge, which, as suggested by the organizers, can be formulated as a
volumetric shape learning task. In this task, the defective skull, the complete
skull and the cranial implant are represented as binary voxel grids. To
accomplish this task, the implant can be either reconstructed directly from the
defective skull or obtained by taking the difference between a defective skull
and a complete skull. In the latter case, a complete skull has to be
reconstructed given a defective skull, which defines a volumetric shape
completion problem. Our baseline approach for this task is based on the former
formulation, i.e., a deep neural network is trained to predict the implants
directly from the defective skulls. The approach generates high-quality
implants in two steps: First, an encoder-decoder network learns a coarse
representation of the implant from down-sampled, defective skulls; The coarse
implant is only used to generate the bounding box of the defected region in the
original high-resolution skull. Second, another encoder-decoder network is
trained to generate a fine implant from the bounded area. On the test set, the
proposed approach achieves an average dice similarity score (DSC) of 0.8555 and
Hausdorff distance (HD) of 5.1825 mm. The code is publicly available at
https://github.com/Jianningli/autoimplant.
