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KMID : 1024020240540010081
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Imaging Science in Dentistry
2024 Volume.54 No. 1 p.81 ~ p.91
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Deep learning-based automatic segmentation of the mandibular canal on panoramic radiographs: A multi-device study
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Moe Thu Zar Aung
Lim Sang-Heon
Han Ji-Yong
Yang Su
Kang Ju-Hee
Kim Jo-Eun
Huh Kyung-Hoe
Yi Won-Jin
Heo Min-Suk
Lee Sam-Sun
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Abstract
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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.
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KEYWORD
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Mandibular Canal, Panoramic Radiography, Deep Learning, Artificial Intelligence
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