Deep learning-based automatic segmentation of the mandibular canal on panoramic radiographs: A multi-device study
Moe Thu Zar Aung, 임상헌, 한지용, 양수, 강주희, 김조은, 허경회, 이원진, 허민석, 이삼선,
소속 상세정보
( Moe Thu Zar Aung ) -
임상헌 ( Lim Sang-Heon ) -
한지용 ( Han Ji-Yong ) -
양수 ( Yang Su ) -
강주희 ( Kang Ju-Hee ) -
김조은 ( Kim Jo-Eun ) -
허경회 ( Huh Kyung-Hoe ) -
이원진 ( Yi Won-Jin ) -
허민석 ( Heo Min-Suk ) -
이삼선 ( Lee Sam-Sun ) -
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.
키워드
Mandibular Canal; Panoramic Radiography; Deep Learning; Artificial Intelligence
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