CT image segmentation for additive manufactured skull implants using deep learning

Abstract

A major challenge in attaining customized additive manufactured (AM) skull implants is segmentation of 3D scans. Therefore, this study aimed to develop a deep learning algorithm (MSDnet) to automatically segment skull defects in computed tomography (CT) scans. The MSDnet was trained with CT scans and corresponding virtual 3D models of patients who had undergone cranioplasty using AM skull implants. The trained MSDnet was able to segment unseen CT scans accurately and quickly. Deep learning can thus remove the barriers of time and effort during CT image segmentation, thereby making customized AM skull implants more affordable and more accessible to clinicians.