Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder-Decoder Network

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dc.authoridSOBAHI, NEBRAS/0000-0001-5788-5629
dc.authoridOzcelik, Salih Taha Alperen/0000-0002-7929-7542
dc.authoridUZEN, Huseyin/0000-0002-0998-2130
dc.authoridSengur, Abdulkadir/0000-0003-1614-2639
dc.contributor.authorOzcelik, Salih Taha Alperen
dc.contributor.authorUzen, Hueseyin
dc.contributor.authorSengur, Abdulkadir
dc.contributor.authorFirat, Hueseyin
dc.contributor.authorTurkoglu, Muammer
dc.contributor.authorCelebi, Adalet
dc.contributor.authorGul, Sema
dc.date.accessioned2025-02-22T14:08:40Z
dc.date.available2025-02-22T14:08:40Z
dc.date.issued2024
dc.departmentDicle Üniversitesien_US
dc.description.abstractBackground: Dental disorders are one of the most important health problems, affecting billions of people all over the world. Early diagnosis is important for effective treatment planning. Precise dental disease segmentation requires reliable tooth numbering, which may be prone to errors if performed manually. These steps can be automated using artificial intelligence, which may provide fast and accurate results. Among the AI methodologies, deep learning has recently shown excellent performance in dental image processing, allowing effective tooth segmentation and numbering. Methods: This paper proposes the Squeeze and Excitation Inception Block-based Encoder-Decoder (SE-IB-ED) network for teeth segmentation in panoramic X-ray images. It combines the InceptionV3 model for encoding with a custom decoder for feature integration and segmentation, using pointwise convolution and an attention mechanism. A dataset of 313 panoramic radiographs from private clinics was annotated using the F & eacute;d & eacute;ration Dentaire Internationale (FDI) system. PSPL and SAM augmented the annotation precision and effectiveness, with SAM automating teeth labeling and subsequently applying manual corrections. Results: The proposed SE-IB-ED network was trained and tested using 80% training and 20% testing of the dataset, respectively. Data augmentation techniques were employed during training. It outperformed the state-of-the-art models with a very high F1-score of 92.65%, mIoU of 86.38%, and 92.84% in terms of accuracy, precision of 92.49%, and recall of 99.92% in the segmentation of teeth. Conclusions: According to the results obtained, the proposed method has great potential for the accurate segmentation of all teeth regions and backgrounds in panoramic X-ray images.en_US
dc.description.sponsorshipFirat University, Scientific Research Project Committee; [TEKF.24.46]en_US
dc.description.sponsorshipThis study was supported by Firat University, Scientific Research Project Committee, under grant no: TEKF.24.46.en_US
dc.identifier.doi10.3390/diagnostics14232719
dc.identifier.issn2075-4418
dc.identifier.issue23en_US
dc.identifier.pmid39682627en_US
dc.identifier.scopus2-s2.0-85211768911en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/diagnostics14232719
dc.identifier.urihttps://hdl.handle.net/11468/29561
dc.identifier.volume14en_US
dc.identifier.wosWOS:001376963700001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofDiagnosticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250222
dc.subjecttooth segmentationen_US
dc.subjecttooth labellingen_US
dc.subjectsqueeze and excitationen_US
dc.subjectattention gateen_US
dc.subjectencoder-decoderen_US
dc.titleEnhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder-Decoder Networken_US
dc.typeArticleen_US

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