Cranial Implant Design Using a Deep Learning Method with Anatomical Regularization

Abstract

Cranioplasty is a surgical operation on the repairing of cranial defects caused by the previous operation, ischemic, or hemorrhagic disease, or even after the removal of cranial tumors. It can be performed by filling the defective area with a range of materials. Interactive and semi-automatic computer-aided design tools for cranial implant design are time-consuming and costly. In this paper, we proposed a deep learning method for automatic cranial implant generation. The proposed method mainly included two steps. First, a variational auto-encoder model was trained to learn the latent distribution of complete skulls. Then, the encoder part of the pre-trained VAE together with an encoder-decoder network was trained to generate the complete skull. We design an anatomical regularization term to drive the predicted skull to be more anatomically plausible compared with the ground truth skull. We evaluated the performance of our method using the skull data from the AutoImplant Challenge. The results show that the proposed framework performs well on the 100 test cases while has poor performance on the 10 test cases.