Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder–Decoder Network
Background: 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...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/23/2719 |
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| author | Salih Taha Alperen Özçelik Hüseyin Üzen Abdulkadir Şengür Hüseyin Fırat Muammer Türkoğlu Adalet Çelebi Sema Gül Nebras M. Sobahi |
| author_facet | Salih Taha Alperen Özçelik Hüseyin Üzen Abdulkadir Şengür Hüseyin Fırat Muammer Türkoğlu Adalet Çelebi Sema Gül Nebras M. Sobahi |
| author_sort | Salih Taha Alperen Özçelik |
| collection | DOAJ |
| description | Background: 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 Fédération 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. |
| format | Article |
| id | doaj-art-20cad18bcb9f4758a83cb6978866e3e4 |
| institution | DOAJ |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-20cad18bcb9f4758a83cb6978866e3e42025-08-20T02:50:37ZengMDPI AGDiagnostics2075-44182024-12-011423271910.3390/diagnostics14232719Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder–Decoder NetworkSalih Taha Alperen Özçelik0Hüseyin Üzen1Abdulkadir Şengür2Hüseyin Fırat3Muammer Türkoğlu4Adalet Çelebi5Sema Gül6Nebras M. Sobahi7Department of Electrical and Electronics Engineering, Faculty of Engineering, Bingöl University, Bingöl 12000, TurkeyDepartment of Computer Engineering, Faculty of Engineering, Bingöl University, Bingöl 12000, TurkeyDepartment of Electrical and Electronic Engineering, Faculty of Technology, Firat University, Elazig 23000, TurkeyDepartment of Computer Engineering, Faculty of Engineering, Dicle University, Diyarbakir 21000, TurkeyDepartment of Software Engineering, Samsun University, Samsun 55000, TurkeyOral and Maxillofacial Surgery Department, Faculty of Dentistry, Mersin University, Mersin 33000, TurkeyDepartment of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Ondokuz Mayis University, Samsun 55000, TurkeyDepartment of Electrical and Electronics Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi ArabiaBackground: 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 Fédération 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.https://www.mdpi.com/2075-4418/14/23/2719tooth segmentationtooth labellingsqueeze and excitationattention gateencoder–decoder |
| spellingShingle | Salih Taha Alperen Özçelik Hüseyin Üzen Abdulkadir Şengür Hüseyin Fırat Muammer Türkoğlu Adalet Çelebi Sema Gül Nebras M. Sobahi Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder–Decoder Network Diagnostics tooth segmentation tooth labelling squeeze and excitation attention gate encoder–decoder |
| title | Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder–Decoder Network |
| title_full | Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder–Decoder Network |
| title_fullStr | Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder–Decoder Network |
| title_full_unstemmed | Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder–Decoder Network |
| title_short | Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder–Decoder Network |
| title_sort | enhanced panoramic radiograph based tooth segmentation and identification using an attention gate based encoder decoder network |
| topic | tooth segmentation tooth labelling squeeze and excitation attention gate encoder–decoder |
| url | https://www.mdpi.com/2075-4418/14/23/2719 |
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