Innovative GCNet Model Enhances Automatic X-ray Teeth Segmentation

Posted: January 2, 2025
The article introduces a novel model, GCNet, for automatic teeth segmentation from X-ray images to address challenges like small dataset sizes and blurred boundaries between teeth and tissue. GCNet, with Grouped Global Attention and Cross-Layer Fusion modules, achieves stable and precise segmentation on small datasets. Experimental results show superior performance compared to existing models, with a Dice coefficient of 0.9338. The model enhances dental image analysis by providing clearer segmentation boundaries.

This article summary was generated by AI. To view the full article, click the link here: https://pubmed.ncbi.nlm.nih.gov/39747360/
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