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多架构深度学习用于CBCT 混合牙中牙硬组织和肉质的细分

Marwa Baraka¹, Elbadry Elbadry², Omer Abourida²

¹Pediatric Dentistry and Dental Public Health Department, Faculty of Dentistry, Alexandria University, Champollion St., El Azareta, Alexandria, Egypt.

Journal of dentistry

|January 10, 2026

概括

深度学习模型在儿科CBCT扫描中准确地细分了牙结构. nnU-Net ResEncM模型显示了在初级牙和永久牙中细分纸和硬组织方面的最佳整体性能.

科学领域:

使用先进的深度学习架构,包括卷积神经网络 (CNN),变压器和Mamba,用于牙科中复杂的图像分析.

背景情况:

儿科圆束计算机断层扫描 (CBCT) 分析需要精确细分牙结构,这耗时且依赖于操作人员.
混合牙由于存在初级和正在发育的永久牙而带来了独特的挑战.

研究的目的:

开发和评估基于深度学习的3D模型,用于在儿科CBCT扫描中自动细分脉,初级和永久牙结构.
为了比较不同深度学习架构 (CNN,变压器,Mamba) 对此细分任务的性能.

主要方法:

分析了151个CBCT扫描 (105个内部,46个外部) 与专家注释的数据用于培训,验证和测试.
训练完全监督的多任务模型,在六个不同的架构中对纸和硬组织结构进行细分.
评估模型使用诸如子相似系数 (DSC),交叉线 (IoU) 和豪斯多夫距离 (HD95) 等指标.

主要成果:

nnU-Net ResEncM模型在分割永久牙 (DSC > 0.98) 和整体结构方面表现出最高的准确性.
初级牙细分的准确性较低,特别是在年龄较大的儿童 (10-13岁) 中,但仍然显示出足够的性能.
U-Mamba Bot和U-Mamba Enc架构显示出有前途,特别是分别用于真皮和初级牙细分.

结论:

深度学习模型,特别是nnU-Net ResEncM,为儿科CBCT中牙结构的自动细分提供了可行和准确的方法.
自动化细分可以显著简化CBCT评估,提高工作流效率,用于管理牙和复杂病例的发展.
该研究为儿童牙科成像中人工智能辅助分析提供了框架,涵盖了各种牙阶段和异常.

关键词:

人工智能的人工智能在CBCT中,CBCT是CBCT.卷积模型是一个卷积模型.深度学习 (Deep Learning) 是一种深度学习.牙细分是指牙的细分.混合的牙是混合的牙儿科牙科儿科牙科变压器模型变压器模型

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