Comparative Analysis of Pixel-Based Segmentation Models for Accurate Detection of Impacted Teeth on Panoramic Radiographs
Accurate detection of impacted teeth in panoramic radiographs is critical for effective diagnosis and treatment planning in dentistry. Traditional segmentation methods often face challenges in achieving accurate detection due to the anatomical complexity and variability of dental structures. This st...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10818474/ |
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| author | Meryem Durmus Burhan Ergen Adalet Celebi Muammer Turkoglu |
| author_facet | Meryem Durmus Burhan Ergen Adalet Celebi Muammer Turkoglu |
| author_sort | Meryem Durmus |
| collection | DOAJ |
| description | Accurate detection of impacted teeth in panoramic radiographs is critical for effective diagnosis and treatment planning in dentistry. Traditional segmentation methods often face challenges in achieving accurate detection due to the anatomical complexity and variability of dental structures. This study aims to address these limitations by performing a comprehensive comparative analysis of four advanced pixel-based segmentation models - U-Net, FPN, PSPNet and LinkNet - integrated with ten different backbone architectures. Using a meticulously annotated dataset of 407 high-resolution panoramic radiographs, the models were rigorously trained and evaluated using robust performance metrics, including accuracy, precision, recall, F1 score, and Intersection over Union (IoU). Among the configurations tested, the U-Net model with an EfficientNetB7 backbone achieved the highest performance, with an average IoU score of 85.29%, demonstrating superior accuracy and reliability. The main contributions of this study are the in-depth comparison of state-of-the-art segmentation models, the identification of the most effective architectures tailored for dental radiograph segmentation, and new insights into the advantages of pixel-based approaches over region-based methods commonly used in previous studies. These findings highlight the strengths and limitations of each model, providing practical guidance for researchers and clinicians in selecting appropriate solutions for impacted teeth detection. In addition, the study highlights the potential for future advances through hybrid approaches and customized model designs to further improve detection accuracy and clinical applicability. As a result, this research demonstrates the transformative potential of integrating artificial intelligence into dental diagnostics, paving the way for more accurate, efficient and scalable solutions to improve clinical decision-making. |
| format | Article |
| id | doaj-art-0f6e364eb25846d98eb8581a6a1de081 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-0f6e364eb25846d98eb8581a6a1de0812025-01-14T00:02:10ZengIEEEIEEE Access2169-35362025-01-01136262627610.1109/ACCESS.2024.352381610818474Comparative Analysis of Pixel-Based Segmentation Models for Accurate Detection of Impacted Teeth on Panoramic RadiographsMeryem Durmus0https://orcid.org/0000-0002-0558-2260Burhan Ergen1https://orcid.org/0000-0003-3244-2615Adalet Celebi2https://orcid.org/0000-0003-2471-1942Muammer Turkoglu3https://orcid.org/0000-0002-2377-4979Distance Education Center, Rectorate, Samsun University, Samsun, TürkiyeDepartment of Computer Engineering, Faculty of Engineering, Firat University, Elâzığ, TürkiyeDepartment of Oral, Dental and Maxillofacial Surgery, Department of Clinical Sciences, Faculty of Dentistry, Mersin University, Mersin, TürkiyeFaculty of Engineering and Natural Sciences, Samsun University, Samsun, TürkiyeAccurate detection of impacted teeth in panoramic radiographs is critical for effective diagnosis and treatment planning in dentistry. Traditional segmentation methods often face challenges in achieving accurate detection due to the anatomical complexity and variability of dental structures. This study aims to address these limitations by performing a comprehensive comparative analysis of four advanced pixel-based segmentation models - U-Net, FPN, PSPNet and LinkNet - integrated with ten different backbone architectures. Using a meticulously annotated dataset of 407 high-resolution panoramic radiographs, the models were rigorously trained and evaluated using robust performance metrics, including accuracy, precision, recall, F1 score, and Intersection over Union (IoU). Among the configurations tested, the U-Net model with an EfficientNetB7 backbone achieved the highest performance, with an average IoU score of 85.29%, demonstrating superior accuracy and reliability. The main contributions of this study are the in-depth comparison of state-of-the-art segmentation models, the identification of the most effective architectures tailored for dental radiograph segmentation, and new insights into the advantages of pixel-based approaches over region-based methods commonly used in previous studies. These findings highlight the strengths and limitations of each model, providing practical guidance for researchers and clinicians in selecting appropriate solutions for impacted teeth detection. In addition, the study highlights the potential for future advances through hybrid approaches and customized model designs to further improve detection accuracy and clinical applicability. As a result, this research demonstrates the transformative potential of integrating artificial intelligence into dental diagnostics, paving the way for more accurate, efficient and scalable solutions to improve clinical decision-making.https://ieeexplore.ieee.org/document/10818474/Backbone networkdeep learningimpacted teeth detectionpanoramic radiographpixel-based segmentation |
| spellingShingle | Meryem Durmus Burhan Ergen Adalet Celebi Muammer Turkoglu Comparative Analysis of Pixel-Based Segmentation Models for Accurate Detection of Impacted Teeth on Panoramic Radiographs IEEE Access Backbone network deep learning impacted teeth detection panoramic radiograph pixel-based segmentation |
| title | Comparative Analysis of Pixel-Based Segmentation Models for Accurate Detection of Impacted Teeth on Panoramic Radiographs |
| title_full | Comparative Analysis of Pixel-Based Segmentation Models for Accurate Detection of Impacted Teeth on Panoramic Radiographs |
| title_fullStr | Comparative Analysis of Pixel-Based Segmentation Models for Accurate Detection of Impacted Teeth on Panoramic Radiographs |
| title_full_unstemmed | Comparative Analysis of Pixel-Based Segmentation Models for Accurate Detection of Impacted Teeth on Panoramic Radiographs |
| title_short | Comparative Analysis of Pixel-Based Segmentation Models for Accurate Detection of Impacted Teeth on Panoramic Radiographs |
| title_sort | comparative analysis of pixel based segmentation models for accurate detection of impacted teeth on panoramic radiographs |
| topic | Backbone network deep learning impacted teeth detection panoramic radiograph pixel-based segmentation |
| url | https://ieeexplore.ieee.org/document/10818474/ |
| work_keys_str_mv | AT meryemdurmus comparativeanalysisofpixelbasedsegmentationmodelsforaccuratedetectionofimpactedteethonpanoramicradiographs AT burhanergen comparativeanalysisofpixelbasedsegmentationmodelsforaccuratedetectionofimpactedteethonpanoramicradiographs AT adaletcelebi comparativeanalysisofpixelbasedsegmentationmodelsforaccuratedetectionofimpactedteethonpanoramicradiographs AT muammerturkoglu comparativeanalysisofpixelbasedsegmentationmodelsforaccuratedetectionofimpactedteethonpanoramicradiographs |