In this study, we present a baseline approach for AutoImplant (https://autoimplant.grand-challenge.org/) – the cranial implant design challenge, which 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 downsampled, 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 codes are available at https://github.com/Jianningli/autoimplant.
|Title of host publication||Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures. CLIP 2020|
|Publication status||Published - 1 Oct 2020|
- deep learning
- skull reconstruction
- shape completion