CranGAN: Adversarial Point Cloud Reconstruction for patient-specific Cranial Implant Design

Abstract

Automatizing cranial implant design has become an increasingly important avenue in biomedical research. Benefits in terms of financial resources, time and patient safety necessitate the formulation of an efficient and accurate procedure for the same. This paper attempts to provide a new research direction to this problem, through an adversarial deep learning solution. Specifically, in this work, we present CranGAN - a 3D Conditional Generative Adversarial Network designed to reconstruct a 3D representation of a complete skull given its defective counterpart. A novel solution of employing point cloud representations instead of conventional 3D meshes and voxel grids is proposed. We provide both qualitative and quantitative analysis of our experiments with three separate GAN objectives, and compare the utility of two 3D reconstruction loss functions viz. Hausdorff Distance and Chamfer Distance. We hope that our work inspires further research in this direction. Clinical relevance— This paper establishes a new research direction to assist in automated implant design for cranioplasty.