[Automatic segmentation of dental cone-beam computed tomography scans using a deep learning framework].

Introduction: The goal of segmentation is to reconstruct cone-beam computed tomography (CBCT) images in three dimensions (3D). In oral surgery and periodontology, digital data processing enables 3D planning of surgical interventions. Commonly used threshold-based segmentation is fast but inaccurate, whereas semi-automatic methods are sufficiently accurate but time-consuming. Recently, with artificial intelligence-based technologies, automatic segmentation of CBCT images has become feasible. Objective: To present a deep learning segmentation model trained on CBCT images derived from clinical practice and to evaluate its efficiency. Method: The study consisted of three phases: establishing the training dataset, training the deep learning model and testing its accuracy. CBCT images of 70, partially edentulous patients were used to establish the training dataset. The deep learning model, based on the SegResNet architecture, was developed within the MONAI framework. To verify the accuracy of the deep learning model, 15 CBCT scans were used processed using the deep learning-based segmentation and semi-automatic segmentation, and the results were compared. Results: The similarity between the two methods, based on intersection over union, was on average 0.91 ± 0.02. The average Dice similarity coefficient was 0.95 ± 0.01, and the average Hausdorff (95%) distance was 0.67 mm ± 0.22 mm. There was no statistically significant difference in the volume of the 3D models segmented by the deep learning architecture compared to those created by semi-automatic segmentation (p = 0.31). Discussion: The deep learning model used in our study performed segmentation of CBCT images with accuracy comparable to other artificial intelligence-based systems reported in the literature. Since the CBCT images were sourced from routine clinical practice, the deep learning model segmented periodontal bone topography and alveolar ridge defects with relatively high reliability. Conclusion: The deep learning model accurately segmented the mandible in dental CBCT scans. Therefore, the deep learning-based 3D models could be suitable for digital planning of reconstructive oral and periodontal surgical interventions. Orv Hetil. 2024; 165(32): 1242–1251.