Cranial Implant Design Through Multiaxial Slice Inpainting Using Deep Learning

Abstract

Cranial implant design can be thought as a 3D shape completion task predicting the missing part of a defective cranium, which is a time-consuming task in traditional methods. This paper proposes a deep convolutional neural network (CNN) based method which predicts the implant from a binary voxel image of a defective skull. Three networks with the same structure are trained for inpainting sagittal, coronal, and horizontal slices of the defective skull, respectively. After skull size regularization and slice extraction, inpainting results from one or more axes are used to synthesize the final binary implant voxel image. Cross-validation shows that the proposed method has a good performance in the cranial implant design task in terms of both Dice similarity coefficient (DSC) and Hausdorff distance (HD).