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Fully Automated AI-Based Digital Workflow for Mirroring of Healthy and Defective Craniofacial Models

Year: 2025

Type: article

Abstract: The accurate reconstruction of craniofacial defects requires the precise segmentation and mirroring of healthy anatomy. Conventional workflows rely on manual interaction, making them time-consuming and subject to operator variability. This study developed and validated a fully automated digital pipeline that integrates deep learning-based segmentation with algorithmic mirroring for craniofacial reconstruction. A total of 388 cranial CT scans were used to train a three-dimensional nnU-Net model for skull and mandible segmentation. A Principal Component Analysis-Iterative Closest Point (PCA-ICP) algorithm was then applied to compute the sagittal symmetry plane and perform mirroring. Automated results were compared with expert-generated segmentations and manually defined symmetry planes using Dice Similarity Coefficient (DSC), Mean Surface Distance (MSD), Hausdorff Distance (HD), and angular deviation. The nnU-Net achieved high segmentation accuracy for both the mandible (mean DSC 0.956) and the skull (mean DSC 0.965). Mirroring results showed minimal angular deviation from expert reference planes (mandible: 1.32° ± 0.71° in defect cases, 1.58° ± 1.12° in intact cases; skull: 1.75° ± 0.84° in defect cases, 1.15° ± 0.81° in intact cases). The presence of defects did not significantly affect accuracy. This automated workflow demonstrated robust performance and clinical applicability, offering standardized, reproducible, and time-efficient planning for craniofacial reconstruction.

Source: Journal of Imaging

DOI: 10.3390/jimaging11110407