Automatic segmentation of inferior alveolar canal with ambiguity classification in panoramic images using deep learning.
Convolution neural network (CNN)
Deep learning
Image segmentation
Inferior alveolar canal (IAC)
Panoramic radiography (PR)
Journal
Heliyon
ISSN: 2405-8440
Titre abrégé: Heliyon
Pays: England
ID NLM: 101672560
Informations de publication
Date de publication:
Feb 2023
Feb 2023
Historique:
received:
10
08
2022
revised:
01
02
2023
accepted:
08
02
2023
entrez:
28
2
2023
pubmed:
1
3
2023
medline:
1
3
2023
Statut:
epublish
Résumé
Manual segmentation of the inferior alveolar canal (IAC) in panoramic images requires considerable time and labor even for dental experts having extensive experience. The objective of this study was to evaluate the performance of automatic segmentation of IAC with ambiguity classification in panoramic images using a deep learning method. Among 1366 panoramic images, 1000 were selected as the training dataset and the remaining 336 were assigned to the testing dataset. The radiologists divided the testing dataset into four groups according to the quality of the visible segments of IAC. The segmentation time, dice similarity coefficient (DSC), precision, and recall rate were calculated to evaluate the efficiency and segmentation performance of deep learning-based automatic segmentation. Automatic segmentation achieved a DSC of 85.7% (95% confidence interval [CI] 75.4%-90.3%), precision of 84.1% (95% CI 78.4%-89.3%), and recall of 87.7% (95% CI 77.7%-93.4%). Compared with manual annotation (5.9s per image), automatic segmentation significantly increased the efficiency of IAC segmentation (33 ms per image). The DSC and precision values of group 4 (most visible) were significantly better than those of group 1 (least visible). The recall values of groups 3 and 4 were significantly better than those of group 1. The deep learning-based method achieved high performance for IAC segmentation in panoramic images under different visibilities and was positively correlated with IAC image clarity.
Sections du résumé
Background
UNASSIGNED
Manual segmentation of the inferior alveolar canal (IAC) in panoramic images requires considerable time and labor even for dental experts having extensive experience. The objective of this study was to evaluate the performance of automatic segmentation of IAC with ambiguity classification in panoramic images using a deep learning method.
Methods
UNASSIGNED
Among 1366 panoramic images, 1000 were selected as the training dataset and the remaining 336 were assigned to the testing dataset. The radiologists divided the testing dataset into four groups according to the quality of the visible segments of IAC. The segmentation time, dice similarity coefficient (DSC), precision, and recall rate were calculated to evaluate the efficiency and segmentation performance of deep learning-based automatic segmentation.
Results
UNASSIGNED
Automatic segmentation achieved a DSC of 85.7% (95% confidence interval [CI] 75.4%-90.3%), precision of 84.1% (95% CI 78.4%-89.3%), and recall of 87.7% (95% CI 77.7%-93.4%). Compared with manual annotation (5.9s per image), automatic segmentation significantly increased the efficiency of IAC segmentation (33 ms per image). The DSC and precision values of group 4 (most visible) were significantly better than those of group 1 (least visible). The recall values of groups 3 and 4 were significantly better than those of group 1.
Conclusions
UNASSIGNED
The deep learning-based method achieved high performance for IAC segmentation in panoramic images under different visibilities and was positively correlated with IAC image clarity.
Identifiants
pubmed: 36852021
doi: 10.1016/j.heliyon.2023.e13694
pii: S2405-8440(23)00901-5
pmc: PMC9957750
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e13694Informations de copyright
© 2023 The Authors.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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