Deep learning driven segmentation of maxillary impacted canine on cone beam computed tomography images

• Published in: Scientific reports Vol. 14; no. 1; pp. 369 - 8
• Main Authors: Swaity, Abdullah, Elgarba, Bahaaeldeen M., Morgan, Nermin, Ali, Saleem, Shujaat, Sohaib, Borsci, Elena, Chilvarquer, Israel, Jacobs, Reinhilde
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 03.01.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/700/3032; 692/700/3032/3093/3094; 692/700/3032/3093/3095; 692/700/3032/3145; Automation; Canine teeth; Computed tomography; Cone-Beam Computed Tomography - methods; Cuspid - diagnostic imaging; Datasets; Deep Learning; Dentistry; Humanities and Social Sciences; Humans; Image Processing, Computer-Assisted - methods; Maxilla; Maxillofacial surgery; Medical research; Medicine; multidisciplinary; Neural networks; Neural Networks, Computer; Orthodontics; Performance assessment; Planning; Science; Science (multidisciplinary); Segmentation; Teeth; Tomography; Tooth, Impacted
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-49613-0

The process of creating virtual models of dentomaxillofacial structures through three-dimensional segmentation is a crucial component of most digital dental workflows. This process is typically performed using manual or semi-automated approaches, which can be time-consuming and subject to observer bias. The aim of this study was to train and assess the performance of a convolutional neural network (CNN)-based online cloud platform for automated segmentation of maxillary impacted canine on CBCT image. A total of 100 CBCT images with maxillary canine impactions were randomly allocated into two groups: a training set (n = 50) and a testing set (n = 50). The training set was used to train the CNN model and the testing set was employed to evaluate the model performance. Both tasks were performed on an online cloud-based platform, ‘Virtual patient creator’ (Relu, Leuven, Belgium). The performance was assessed using voxel- and surface-based comparison between automated and semi-automated ground truth segmentations. In addition, the time required for segmentation was also calculated. The automated tool showed high performance for segmenting impacted canines with a dice similarity coefficient of 0.99 ± 0.02. Moreover, it was 24 times faster than semi-automated approach. The proposed CNN model achieved fast, consistent, and precise segmentation of maxillary impacted canines.