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基于深度学习的牙图像细分和根管测量

Ziqing Chen¹, Qi Liu¹, Jialei Wang²

¹School of Biomedical Engineering, Sichuan University, Chengdu, China.

Frontiers in bioengineering and biotechnology

|June 24, 2025

概括

使用束计算机断层扫描 (CBCT) 图像实现牙细分和根管测量的自动化方法达到高精度. 这种人工智能驱动的方法提供客观,高效的结果,用于临床指导内牙程序.

科学领域:

医疗成像医学成像
人工智能在牙科中的应用
牙周内科医院牙周内科医院牙周内科医院

背景情况:

精确的牙细分和根管测量对于内牙诊断和治疗规划至关重要.
手动测量可能耗时,容易引起观察者之间的变化.
圆束计算断层扫描 (CBCT) 为牙科应用提供了详细的3D解剖数据.

研究的目的:

开发一种使用CBCT图像进行牙细分和根管测量的自动化方法.
为临床指导提供客观,高效和准确的测量.
协助临床医生进行根管诊断分级,仪器选择和术前计划.

主要方法:

利用了注意力U-Net用于牙描述符识别和感兴趣区域 (ROI) 裁剪.
应用了一种集成的深度学习方法,用于自动化牙细分.
实现了位置校正和根管长度和角度的自动测量.

主要成果:

实现了96.33%的子系数和92.94%的贾卡德系数的细分.
根管长度测量的相对误差为3.42%,低于现有方法.
临床医生认识到自动校正对测量准确性的积极影响.

结论:

拟议的自动化细分方法显示了高性能和准确性.
与手动测量相比,自动测量系统提供了较低的相对误差.
这种人工智能驱动的方法为增强内牙科临床应用提供了有价值的工具.

关键词:

注意U-net的注意事项在CBCT中,CBCT是CBCT.这就是V-Net.深度学习是一种深度学习.根管测量根管测量牙实例细分牙实例细分

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