Automatic X-ray teeth segmentation with grouped attention
Abstract Detection and teeth segmentation from X-rays, aiding healthcare professionals in accurately determining the shape and growth trends of teeth. However, small dataset sizes due to patient privacy, high noise, and blurred boundaries between periodontal tissue and teeth pose challenges to the m...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-84629-0 |
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| _version_ | 1841559693298761728 |
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| author | Wenjin Zhong XiaoXiao Ren HanWen Zhang |
| author_facet | Wenjin Zhong XiaoXiao Ren HanWen Zhang |
| author_sort | Wenjin Zhong |
| collection | DOAJ |
| description | Abstract Detection and teeth segmentation from X-rays, aiding healthcare professionals in accurately determining the shape and growth trends of teeth. However, small dataset sizes due to patient privacy, high noise, and blurred boundaries between periodontal tissue and teeth pose challenges to the models’ transportability and generalizability, making them prone to overfitting. To address these issues, we propose a novel model, named Grouped Attention and Cross-Layer Fusion Network (GCNet). GCNet effectively handles numerous noise points and significant individual differences in the data, achieving stable and precise segmentation on small-scale datasets. The model comprises two core modules: Grouped Global Attention (GGA) modules and Cross-Layer Fusion (CLF) modules. The GGA modules capture and group texture and contour features, while the CLF modules combine these features with deep semantic information to improve prediction. Experimental results on the Children’s Dental Panoramic Radiographs dataset show that our model outperformed existing models such as GT-U-Net and Teeth U-Net, with a Dice coefficient of 0.9338, sensitivity of 0.9426, and specificity of 0.9821. The GCNet model also demonstrates clearer segmentation boundaries compared to other models. |
| format | Article |
| id | doaj-art-cfc9204309684b0ebf1c994e9286c6a6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cfc9204309684b0ebf1c994e9286c6a62025-01-05T12:16:43ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-024-84629-0Automatic X-ray teeth segmentation with grouped attentionWenjin Zhong0XiaoXiao Ren1HanWen Zhang2Macquarie UniversityThe University of New South WalesThe University of New South WalesAbstract Detection and teeth segmentation from X-rays, aiding healthcare professionals in accurately determining the shape and growth trends of teeth. However, small dataset sizes due to patient privacy, high noise, and blurred boundaries between periodontal tissue and teeth pose challenges to the models’ transportability and generalizability, making them prone to overfitting. To address these issues, we propose a novel model, named Grouped Attention and Cross-Layer Fusion Network (GCNet). GCNet effectively handles numerous noise points and significant individual differences in the data, achieving stable and precise segmentation on small-scale datasets. The model comprises two core modules: Grouped Global Attention (GGA) modules and Cross-Layer Fusion (CLF) modules. The GGA modules capture and group texture and contour features, while the CLF modules combine these features with deep semantic information to improve prediction. Experimental results on the Children’s Dental Panoramic Radiographs dataset show that our model outperformed existing models such as GT-U-Net and Teeth U-Net, with a Dice coefficient of 0.9338, sensitivity of 0.9426, and specificity of 0.9821. The GCNet model also demonstrates clearer segmentation boundaries compared to other models.https://doi.org/10.1038/s41598-024-84629-0 |
| spellingShingle | Wenjin Zhong XiaoXiao Ren HanWen Zhang Automatic X-ray teeth segmentation with grouped attention Scientific Reports |
| title | Automatic X-ray teeth segmentation with grouped attention |
| title_full | Automatic X-ray teeth segmentation with grouped attention |
| title_fullStr | Automatic X-ray teeth segmentation with grouped attention |
| title_full_unstemmed | Automatic X-ray teeth segmentation with grouped attention |
| title_short | Automatic X-ray teeth segmentation with grouped attention |
| title_sort | automatic x ray teeth segmentation with grouped attention |
| url | https://doi.org/10.1038/s41598-024-84629-0 |
| work_keys_str_mv | AT wenjinzhong automaticxrayteethsegmentationwithgroupedattention AT xiaoxiaoren automaticxrayteethsegmentationwithgroupedattention AT hanwenzhang automaticxrayteethsegmentationwithgroupedattention |