Journal of Prosthodontics News
Trueness of Artificial Intelligence-Based, Manual, and Global Thresholding Segmentation Protocols for Human Mandibles
Now online in the Journal of Prosthodontics, an article comparing the trueness of different segmentation protocols co-authored by ACP fellows Chao-Chieh Yang DDS, MSD, FACP and Wei-Shao Lin DDS, PhD, MBA, FACP.
Segmentation involves partitioning scanned images to isolate and define boundaries within regions of interest. Accurate segmentation results in 3D models crucial for diagnosis and treatment planning. Different segmentation methods include manual segmentation, global thresholding, and AI-based segmentation. AI-based segmentation, in particular, is underexplored in the literature. Therefore, this study aimed to evaluate the trueness of AI-based segmentation by comparing it with global thresholding and manual segmentation, using superimposition and comparison against a reference gold standard surface scan model.
A cone beam computed tomography (CBCT) scanner was used to scan 12 dry human mandibles, and the acquired digital imaging and communications in medicine (DICOM) files were segmented using three protocols: global thresholding, manual, and AI-based segmentation (Diagnocat). The segmented files were exported as study 3D models. A structured light surface scanner was used to scan all mandibles, and the resulting reference 3D models were exported. The study 3D models were compared with the respective reference 3D models using a mesh comparison software. Root mean square (RMS) error values were recorded to measure the magnitude of deviation (trueness), and color maps were obtained to visualize the differences.
The authors found that AI-based segmentations produced lower RMS values, indicating truer 3D models, compared to global thresholding, and showed no significant differences in some areas compared to manual segmentation. Thus, AI-based segmentation offers a level of segmentation trueness acceptable for use as an alternative to manual or global thresholding segmentation protocols.
Hernandez AKT, Dutra V, Chu T-MG, Yang C-C, Lin W-S. Trueness of artificial intelligence-based, manual, and global thresholding segmentation protocols for human mandibles. J Prosthodont. 2025; 1–8. https://doi.org/10.1111/jopr.70008
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