SegmentAnyTooth:一个开源的深度学习框架,用于在口内照片中对牙进行计数和细分
Khoa Dang Nguyen1, Hung Trong Hoang2, Thi-Phuong Hong Doan1
1College of Dentistry, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Journal of dental sciences
|April 14, 2025
概括
一个人工智能工具SegmentAnyTooth精确地从口腔内照片中对牙进行细分. 这种自动化牙科分析改善了获得预防护理的机会,并支持公共卫生倡议.
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
- 深度学习用于口腔健康
背景情况:
- 预防性牙科对于公共口腔健康至关重要.
- 在获得牙科护理方面的不平等仍然存在,特别是在服务不足的地区.
- 使用口腔内照片进行基于图像的牙科分析提供了一个可扩展的解决方案.
研究的目的:
- 开发SegmentAnyTooth,这是一个开源的深度学习框架,用于自动化牙计数和细分.
- 为了应对五种标准口腔内视图中准确识别牙的挑战.
- 为先进的牙科应用奠定基础,并提高获得预防性护理的机会.
主要方法:
- 来自1000组的5000张口内照片的数据集被注释.
- 你只看一次11 (YOLO11) 纳米模型用于牙定位和计数.
- 轻细分 任何高质量 (轻HQ-SAM) 具有主动学习的东西都被用于细分.
主要成果:
- SegmentAnyTooth在多个视图中实现了高分段精度 (例如,上部闭塞的DSC为0.983±0.036).
- 模型在侧向视图上表现出强的表现 (DSCs为0.939 ± 0.070右,0.945 ± 0.056左).
- 与基线模型 (U-Net,nnU-Net,Mask R-CNN;P <0.01) 相比,观察到有统计学意义的改善.
结论:
- SegmentAnyTooth提供精确的多视图牙细分,以推进牙科护理和诊断.
- 开源框架有助于整合到临床和公共卫生工作流程中.
- 未来的工作重点是提高SegmentAnyTooth模型的通用性.
相关概念视频
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