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<p>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.</p>

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Automatic X-ray teeth segmentation with grouped attention

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月2日

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  • Wenjin Zhong,
  • XiaoXiao Ren,
  • HanWen Zhang

摘要

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.