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- Title
Deep learning driven segmentation of maxillary impacted canine on cone beam computed tomography images.
- Authors
Swaity, Abdullah; Elgarba, Bahaaeldeen M.; Morgan, Nermin; Ali, Saleem; Shujaat, Sohaib; Borsci, Elena; Chilvarquer, Israel; Jacobs, Reinhilde
- Abstract
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.
- Subjects
CONE beam computed tomography; CONVOLUTIONAL neural networks; DEEP learning; IMAGE reconstruction algorithms; SIMULATED patients
- Publication
Scientific Reports, 2024, Vol 13, Issue 1, p1
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-023-49613-0