Convolutional neural network‐based automated maxillary alveolar bone segmentation on cone‐beam computed tomography images

Abstract

AbstractObjectivesTo develop and assess the performance of a novel artificial intelligence (AI)‐driven convolutional neural network (CNN)‐based tool for automated three‐dimensional (3D) maxillary alveolar bone segmentation on cone‐beam computed tomography (CBCT) images.Materials and MethodsA total of 141 CBCT scans were collected for performing training (n = 99), validation (n = 12), and testing (n = 30) of the CNN model for automated segmentation of the maxillary alveolar bone and its crestal contour. Following automated segmentation, the 3D models with under‐ or overestimated segmentations were refined by an expert for generating a refined‐AI (R‐AI) segmentation. The overall performance of CNN model was assessed. Also, 30% of the testing sample was randomly selected and manually segmented to compare the accuracy of AI and manual segmentation. Additionally, the time required to generate a 3D model was recorded in seconds (s).ResultsThe accuracy metrics of automated segmentation showed an excellent range of values for all accuracy metrics. However, the manual method (95% HD: 0.20 ± 0.05 mm; IoU: 95% ± 3.0; DSC: 97% ± 2.0) showed slightly better performance than the AI segmentation (95% HD: 0.27 ± 0.03 mm; IoU: 92% ± 1.0; DSC: 96% ± 1.0). There was a statistically significant difference of the time‐consumed among the segmentation methods (p < .001). The AI‐driven segmentation (51.5 ± 10.9 s) was 116 times faster than the manual segmentation (5973.3 ± 623.6 s). The R‐AI method showed intermediate time‐consumed (1666.7 ± 588.5 s).ConclusionAlthough the manual segmentation showed slightly better performance, the novel CNN‐based tool also provided a highly accurate segmentation of the maxillary alveolar bone and its crestal contour consuming 116 times less than the manual approach.