Towards fully automated third molar development staging in panoramic radiographs

dc.contributor.authorBanar, Nikolay
dc.contributor.authorBertels, Jeroen
dc.contributor.authorLaurent, Francois
dc.contributor.authorBoedi, Rizky Merdietio
dc.contributor.authorDe Tobel, Jannick
dc.contributor.authorThevissen, Patrick
dc.contributor.authorVandermeulen, Dirk
dc.date.accessioned2020-12-31T07:00:57Z
dc.date.issued2020-09
dc.description.abstractStaging 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.en_ZA
dc.description.departmentAnatomyen_ZA
dc.description.embargo2021-04-01
dc.description.librarianhj2020en_ZA
dc.description.sponsorshipInternal Funds KU Leuvenen_ZA
dc.description.urihttp://link.springer.com/journal/414en_ZA
dc.identifier.citationBanar, N., Bertels, J., Laurent, F. et al. Towards fully automated third molar development staging in panoramic radiographs. International Journal of Legal Medicine 134, 1831–1841 (2020). https://doi.org/10.1007/s00414-020-02283-3.en_ZA
dc.identifier.issn0937-9827 (print)
dc.identifier.issn1437-1596 (online)
dc.identifier.other10.1007/s00414-020-02283-3
dc.identifier.urihttp://hdl.handle.net/2263/77907
dc.language.isoenen_ZA
dc.publisherSpringeren_ZA
dc.rights© Springer-Verlag GmbH Germany, part of Springer Nature 2020. The original publication is available at : http://link.springer.comjournal/414.en_ZA
dc.subjectConvolutional neural network (CNN)en_ZA
dc.subjectRegion of interest (ROI)en_ZA
dc.subjectDental age assessmenten_ZA
dc.subjectThird molaren_ZA
dc.subjectDevelopmental stageen_ZA
dc.subjectLocalizationen_ZA
dc.subjectSegmentationen_ZA
dc.subjectClassificationen_ZA
dc.subject.otherHealth sciences articles SDG-03
dc.subject.otherSDG-03: Good health and well-being
dc.titleTowards fully automated third molar development staging in panoramic radiographsen_ZA
dc.typePostprint Articleen_ZA

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