An enhanced tooth segmentation and numbering according to FDI notation in bitewing radiographs

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dc.authoridYasa, Yasin/0000-0002-4388-2125
dc.authoridOzcan, Caner/0000-0002-2854-4005
dc.authoridKazangirler, Buse Yaren/0000-0002-8690-2042
dc.contributor.authorTekin, Buse Yaren
dc.contributor.authorOzcan, Caner
dc.contributor.authorPekince, Adem
dc.contributor.authorYasa, Yasin
dc.date.accessioned2024-09-29T15:55:11Z
dc.date.available2024-09-29T15:55:11Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractBitewing radiographic imaging is an excellent diagnostic tool for detecting caries and restorations that are difficult to view in the mouth, particularly at the molar surfaces. Labeling radiological images by an expert is a labor-intensive, time-consuming, and meticulous process. A deep learning-based approach has been applied in this study so that experts can perform dental analyzes successfully, quickly, and efficiently. Computer-aided applications can now detect teeth and number classes in bitewing radiographic images automatically. In the deep learning-based approach of the study, the neural network has a structure that works according to regions. A region-based automatic segmentation system that segments each tooth using masks to help to assist analysis as given to lessen the effort of experts. To acquire precision and recall on a test dataset, Intersection Over Union value is determined by comparing the model's classified and ground-truth boxes. The chosen IOU value was set to 0.9 to allocate bounding boxes to the class scores. Mask R-CNN is a method that serves as instance segmentation and predicts a pixel-to-pixel segmentation mask when applied to each Region of Interest. The tooth numbering module uses the FDI notation, which is widely used by dentists, to classify and number dental items found as a result of segmentation. According to the experimental results were reached 100% precision and 97.49% mAP value. In the tooth numbering, were obtained 94.35% precision and 91.51% as an mAP value. The performance of the Mask R-CNN method used has been proven by comparing it with other state-of-the-art methods.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [2200272]en_US
dc.description.sponsorshipThis study is funded by the Scientific and Technological Research Council of Turkey (TUBITAK) as part of the DentiAssist project numbered 2200272.en_US
dc.identifier.doi10.1016/j.compbiomed.2022.105547
dc.identifier.issn0010-4825
dc.identifier.issn1879-0534
dc.identifier.pmid35544975en_US
dc.identifier.scopus2-s2.0-85129526426en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2022.105547
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4511
dc.identifier.volume146en_US
dc.identifier.wosWOS:000804709400007en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDental bitewing radiographen_US
dc.subjectConvolutional neural networksen_US
dc.subjectFdi notationen_US
dc.subjectTooth numberingen_US
dc.subjectInstance segmentationen_US
dc.titleAn enhanced tooth segmentation and numbering according to FDI notation in bitewing radiographsen_US
dc.typeArticleen_US

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