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Towards fully automated third molar development staging in panoramic radiographs

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Abstract
Staging third molar development is commonly used for age assessment in sub-adults. Current staging techniques are, at most, semi-automated and rely on manual interactions prone to operator variability. The aim of this study was to fully automate the staging process by employing the full potential of deep learning, using convolutional neural networks (CNNs) in every step of the procedure. The dataset used to train the CNNs consisted of 400 panoramic radiographs (OPGs), with 20 OPGs per developmental stage per sex, staged in consensus between three observers. The concepts of transfer learning, using pre-trained CNNs, and data augmentation were used to mitigate the issues when dealing with a limited dataset. In this work, a three-step procedure was proposed and the results were validated using fivefold cross-validation. First, a CNN localized the geometrical center of the lower left third molar, around which a square region of interest (ROI) was extracted. Second, another CNN segmented the third molar within the ROI. Third, a final CNN used both the ROI and the segmentation to classify the third molar into its developmental stage. The geometrical center of the third molar was found with an average Euclidean distance of 63 pixels. Third molars were segmented with an average Dice score of 93%. Finally, the developmental stages were classified with an accuracy of 54%, a mean absolute error of 0.69 stages, and a linear weighted Cohen's kappa coefficient of 0.79. The entire automated workflow on average took 2.72 s to compute, which is substantially faster than manual staging starting from the OPG. Taking into account the limited dataset size, this pilot study shows that the proposed fully automated approach shows promising results compared with manual staging.
Keywords
Classification, Dental age assessment, Developmental stage, Localization, Segmentation, Third molar, BONE-AGE ASSESSMENT, SYSTEM

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MLA
Banar, Nikolay, et al. “Towards Fully Automated Third Molar Development Staging in Panoramic Radiographs.” INTERNATIONAL JOURNAL OF LEGAL MEDICINE, vol. 134, no. 5, 2020, pp. 1831–41, doi:10.1007/s00414-020-02283-3.
APA
Banar, N., Bertels, J., Laurent, F., Merdietio Boedi, R., De Tobel, J., Thevissen, P., & Vandermeulen, D. (2020). Towards fully automated third molar development staging in panoramic radiographs. INTERNATIONAL JOURNAL OF LEGAL MEDICINE, 134(5), 1831–1841. https://doi.org/10.1007/s00414-020-02283-3
Chicago author-date
Banar, Nikolay, Jeroen Bertels, François Laurent, Rizky Merdietio Boedi, Jannick De Tobel, Patrick Thevissen, and Dirk Vandermeulen. 2020. “Towards Fully Automated Third Molar Development Staging in Panoramic Radiographs.” INTERNATIONAL JOURNAL OF LEGAL MEDICINE 134 (5): 1831–41. https://doi.org/10.1007/s00414-020-02283-3.
Chicago author-date (all authors)
Banar, Nikolay, Jeroen Bertels, François Laurent, Rizky Merdietio Boedi, Jannick De Tobel, Patrick Thevissen, and Dirk Vandermeulen. 2020. “Towards Fully Automated Third Molar Development Staging in Panoramic Radiographs.” INTERNATIONAL JOURNAL OF LEGAL MEDICINE 134 (5): 1831–1841. doi:10.1007/s00414-020-02283-3.
Vancouver
1.
Banar N, Bertels J, Laurent F, Merdietio Boedi R, De Tobel J, Thevissen P, et al. Towards fully automated third molar development staging in panoramic radiographs. INTERNATIONAL JOURNAL OF LEGAL MEDICINE. 2020;134(5):1831–41.
IEEE
[1]
N. Banar et al., “Towards fully automated third molar development staging in panoramic radiographs,” INTERNATIONAL JOURNAL OF LEGAL MEDICINE, vol. 134, no. 5, pp. 1831–1841, 2020.
@article{8671443,
  abstract     = {Staging third molar development is commonly used for age assessment in sub-adults. Current staging techniques are, at most, semi-automated and rely on manual interactions prone to operator variability. The aim of this study was to fully automate the staging process by employing the full potential of deep learning, using convolutional neural networks (CNNs) in every step of the procedure. The dataset used to train the CNNs consisted of 400 panoramic radiographs (OPGs), with 20 OPGs per developmental stage per sex, staged in consensus between three observers. The concepts of transfer learning, using pre-trained CNNs, and data augmentation were used to mitigate the issues when dealing with a limited dataset. In this work, a three-step procedure was proposed and the results were validated using fivefold cross-validation. First, a CNN localized the geometrical center of the lower left third molar, around which a square region of interest (ROI) was extracted. Second, another CNN segmented the third molar within the ROI. Third, a final CNN used both the ROI and the segmentation to classify the third molar into its developmental stage. The geometrical center of the third molar was found with an average Euclidean distance of 63 pixels. Third molars were segmented with an average Dice score of 93%. Finally, the developmental stages were classified with an accuracy of 54%, a mean absolute error of 0.69 stages, and a linear weighted Cohen's kappa coefficient of 0.79. The entire automated workflow on average took 2.72 s to compute, which is substantially faster than manual staging starting from the OPG. Taking into account the limited dataset size, this pilot study shows that the proposed fully automated approach shows promising results compared with manual staging.},
  author       = {Banar, Nikolay and Bertels, Jeroen and Laurent, François and Merdietio Boedi, Rizky and De Tobel, Jannick and Thevissen, Patrick and Vandermeulen, Dirk},
  issn         = {0937-9827},
  journal      = {INTERNATIONAL JOURNAL OF LEGAL MEDICINE},
  keywords     = {Classification,Dental age assessment,Developmental stage,Localization,Segmentation,Third molar,BONE-AGE ASSESSMENT,SYSTEM},
  language     = {eng},
  number       = {5},
  pages        = {1831--1841},
  title        = {Towards fully automated third molar development staging in panoramic radiographs},
  url          = {http://dx.doi.org/10.1007/s00414-020-02283-3},
  volume       = {134},
  year         = {2020},
}

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