Deep learning-based automatic segmentation of the mandibular canal on panoramic radiographs: A multi-device study
- Author:
Moe Thu Zar AUNG
1
;
Sang-Heon LIM
;
Jiyong HAN
;
Su YANG
;
Ju-Hee KANG
;
Jo-Eun KIM
;
Kyung-Hoe HUH
;
Won-Jin YI
;
Min-Suk HEO
;
Sam-Sun LEE
Author Information
- Publication Type:Original Articles
- From:Imaging Science in Dentistry 2024;54(1):81-91
- CountryRepublic of Korea
- Language:EN
-
Abstract:
Purpose:The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs.
Materials and Methods:A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset.
Results:Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%.
Conclusion:This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.
