Tooth segmentation in 3D cone-beam CT images using deep convolutional neural network

• Published in: Neural network world Vol. 32; no. 6; p. 301
• Main Authors: Khan, S, Mukati, A, Rizvi S.S.H., Yazdanie, N
• Format: Journal Article
• Language: English
• Published: Prague Czech Technical University in Prague, Faculty of Transportation Sciences 01.01.2022
• Subjects: Artificial neural networks; Availability; Computed tomography; Deep learning; Domains; Health care facilities; Image segmentation; Labeling; Machine learning; Neural networks; Orthodontics; Radiographs; Teeth
• ISSN: 1210-0552; 2336-4335
• DOI: 10.14311/nnw.2022.32.018

Segmentation of an individual tooth in dental radiographs has great significance in the process of orthodontics surgeries and dentistry. Machine learning techniques, especially deep convolutional neural networks can play a key role in revolutionizing the way orthodontics surgeons and dentists work. Lately, many researchers have been working on tooth segmentation in 3D volumetric dental scans with a great degree of success, but to the best of our knowledge, there is no pretrained neural network available publicly for performing tooth segmentation in 3D cone-beam dental CT scans. The methods which so far have been proposed by the researchers in this domain are based on complex multistep pipelines. This lack of the availability of a pre-trained model blocks the path for further explorations in this domain. In this research, we have produced a deep learning model for tooth segmentation from CBCT dental radiographs. The proposed model can segment teeth in CBCT scans in a single step. To train the proposed model, we obtained a dataset consisting of 70 3D CBCT volumes from a local health facility. We labeled the ground truth through a semi-automatic method and trained our neural network. The training yielded a validation accuracy of 95.57% on a binary class semantic segmentation of the 3D CBCT volumes. The model is successfully able to segment teeth, regardless of their type from the background in a single step. This eliminates the need of having a complex and lengthy pipeline which many researchers have been proposing. The proposed model can be extended by incorporating labeling schemes. The custom labeling schemes will help healthcare professionals to perform the labeling as per their needs. The produced model can also provide a basis for further research in this domain.