Realistic Cranial Defect Design and Integration Approach in CT-Based Segmented Skulls

Abstract

Machine learning is playing an increasingly important role in completing and analyzing missing or damaged regions in cranial reconstruction. In this paper, we propose an approach that embeds realistically generated synthetic defects into healthy skull samples after reducing the boundary coordinates of manually designed implants to two dimensions via Principal Component Analysis (PCA). Experiments on eleven different implant samples demonstrate that more than 98% of the variance is preserved after PCA, thereby confirming that anatomical consistency is largely maintained. This realistic approach aims to overcome the limited diversity in existing datasets and enhance the performance of machine learning-based methods for defect modeling. Our comprehensive and anatomically realistic synthetic dataset makes a significant contribution to cranial reconstruction processes and provides a solid foundation for deep learning-driven solutions.