Artificial Intelligence for Fast and Accurate 3-Dimensional Tooth Segmentation on Cone-beam Computed Tomography

• Published in: Journal of endodontics Vol. 47; no. 5; pp. 827 - 835
• Main Authors: Lahoud, Pierre, EzEldeen, Mostafa, Beznik, Thomas, Willems, Holger, Leite, André, Van Gerven, Adriaan, Jacobs, Reinhilde
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.05.2021
• Subjects: Artificial Intelligence; computed tomography; Cone-Beam Computed Tomography; convolutional neural network; deep learning; Dentistry; digital imaging/radiology; Medicin och hälsovetenskap; Tooth; Tooth Root; computed tomography; deep learning; Artificial intelligence; convolutional neural network; digital imaging/radiology
• ISSN: 0099-2399; 1878-3554; 1878-3554
• DOI: 10.1016/j.joen.2020.12.020

Tooth segmentation on cone-beam computed tomographic (CBCT) imaging is a labor-intensive task considering the limited contrast resolution and potential disturbance by various artifacts. Fully automated tooth segmentation cannot be achieved by merely relying on CBCT intensity variations. This study aimed to develop and validate an artificial intelligence (AI)-driven tool for automated tooth segmentation on CBCT imaging. A total of 433 Digital Imaging and Communications in Medicine images of single- and double-rooted teeth randomly selected from 314 anonymized CBCT scans were imported and manually segmented. An AI-driven tooth segmentation algorithm based on a feature pyramid network was developed to automatically detect and segment teeth, replacing manual user contour placement. The AI-driven tool was evaluated based on volume comparison, intersection over union, the Dice score coefficient, morphologic surface deviation, and total segmentation time. Overall, AI-driven and clinical reference segmentations resulted in very similar segmentation volumes. The mean intersection over union for full-tooth segmentation was 0.87 (±0.03) and 0.88 (±0.03) for semiautomated (SA) (clinical reference) versus fully automated AI-driven (F-AI) and refined AI-driven (R-AI) tooth segmentation, respectively. R-AI and F-AI segmentation showed an average median surface deviation from SA segmentation of 9.96 μm (±59.33 μm) and 7.85 μm (±69.55 μm), respectively. SA segmentations of single- and double-rooted teeth had a mean total time of 6.6 minutes (±76.15 seconds), F-AI segmentation of 0.5 minutes (±8.64 seconds, 12 times faster), and R-AI segmentation of 1.2 minutes (±33.02 seconds, 6 times faster). This study showed a unique fast and accurate approach for AI-driven automated tooth segmentation on CBCT imaging. These results may open doors for AI-driven applications in surgical and treatment planning in oral health care.