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

• Published in: Clinical oral investigations Vol. 26; no. 5; pp. 3987 - 3998
• Main Authors: Hung, Kuo Feng, Ai, Qi Yong H., King, Ann D., Bornstein, Michael M., Wong, Lun M., Leung, Yiu Yan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2022; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computed tomography; Cone-Beam Computed Tomography - methods; Cysts; Dentistry; Maxillary sinus; Maxillary Sinus - diagnostic imaging; Medicine; Mucosa; Mucous Membrane; Neural networks; Neural Networks, Computer; Original Article; Segmentation; Sinuses; Tomography; Mucosal thickening; Cone-beam computed tomography; Artificial intelligence; Mucosal retention cyst; Convolutional neural network; Maxillary sinus
• ISSN: 1436-3771; 1432-6981; 1436-3771
• DOI: 10.1007/s00784-021-04365-x

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.