We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Automatic detection and segmentation of morphological changes of the maxillary sinus mucosa on cone-beam computed tomography images using a three-dimensional convolutional neural network.
- Authors
Hung, Kuo Feng; Ai, Qi Yong H.; King, Ann D.; Bornstein, Michael M.; Wong, Lun M.; Leung, Yiu Yan
- Abstract
Objectives: To propose and evaluate a convolutional neural network (CNN) algorithm for automatic detection and segmentation of mucosal thickening (MT) and mucosal retention cysts (MRCs) in the maxillary sinus on low-dose and full-dose cone-beam computed tomography (CBCT). Materials and methods: A total of 890 maxillary sinuses on 445 CBCT scans were analyzed. The air space, MT, and MRCs in each sinus were manually segmented. Low-dose CBCTs were divided into training, training-monitoring, and testing datasets at a 7:1:2 ratio. Full-dose CBCTs were used as a testing dataset. A three-step CNN algorithm built based on V-Net and support vector regression was trained on low-dose CBCTs and tested on the low-dose and full-dose datasets. Performance for detection of MT and MRCs using area under the curves (AUCs) and for segmentation using Dice similarity coefficient (DSC) was evaluated. Results: For the detection of MT and MRCs, the algorithm achieved AUCs of 0.91 and 0.84 on low-dose scans and of 0.89 and 0.93 on full-dose scans, respectively. The median DSCs for segmenting the air space, MT, and MRCs were 0.972, 0.729, and 0.678 on low-dose scans and 0.968, 0.663, and 0.787 on full-dose scans, respectively. There were no significant differences in the algorithm performance between low-dose and full-dose CBCTs. Conclusions: The proposed CNN algorithm has the potential to accurately detect and segment MT and MRCs in maxillary sinus on CBCT scans with low-dose and full-dose protocols. Clinical relevance: An implementation of this artificial intelligence application in daily practice as an automated diagnostic and reporting system seems possible.
- Subjects
CONE beam computed tomography; CONVOLUTIONAL neural networks; MAXILLARY sinus; THREE-dimensional imaging; MUCOUS membranes
- Publication
Clinical Oral Investigations, 2022, Vol 26, Issue 5, p3987
- ISSN
1432-6981
- Publication type
Article
- DOI
10.1007/s00784-021-04365-x