开发和评估一个专注的U-net模型,用于对牙的二进制细分与背景在全景X射线图中用于正应用
Miltiadis A Makrygiannakis1,2, Apostolos Ntokos3, Eleftherios G Kaklamanos2,4,5
1School of Dentistry, National and Kapodistrian University of Athens, 2 Thivon str., Athens 11527, Greece.
European journal of orthodontics
|February 10, 2026
概括
一个专注于注意力的U-Net (Att-U-Net) 模型在全景X射线 (OPG) 中准确地划分牙,为牙科AI建立了基准. 这种深度学习方法改善了在牙矯正中自动诊断和治疗计划.
科学领域:
- 人工智能在牙科中的应用
- 医学图像分析 医学图像分析
- 对于医学成像的深度学习
背景情况:
- 全景X射线 (OPG) 对于正牙评估至关重要.
- 在OPG中准确的牙细分对于自动诊断和治疗规划至关重要.
- 基于深度学习的牙图像分析的标准化基准有限.
研究的目的:
- 在OPG中建立牙与背景的二元语义细分的性能基准.
- 为此细分任务应用一个关注的U-Net (Att-U-Net) 架构.
- 为先进的正牙人工智能应用提供基础.
主要方法:
- 在"牙X射线图像上的牙细分"数据集 (598个OPG) 上使用了Att-U-Net模型.
- 员工分层抽样培训 (70%),验证 (10%) 和测试 (20%) 集.
- 通过结合的二元交叉和子损失函数进行训练,以处理类不平衡.
主要成果:
- 在测试组中获得0.911的子系数,0.838的平均IOU和0.974的像素精度.
- 在具有挑战性的细分案例中表现出稳定的收和改进的性能.
- 通过视觉精确的细分面具,表现出快速稳定的推理时间.
结论:
- 在OPG中,Att-U-Net模型提供了强大的和可靠的牙细分.
- 高度的定量和质量性能支持其在OPG质量审查中的实用性.
- 这项工作为更复杂的细分任务和在正牙科中增强的自动诊断系统奠定了基础.
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