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相关实验视频

Updated: May 10, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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在多模式图像上的牙细分使用适应的细分任何模型.

Peijuan Wang1,2, Hanjie Gu1,2, Yuliang Sun3

  • 1College of Information Science and Technology, Zhejiang Shuren University, Hangzhou, 310015, China.

Scientific reports
|April 22, 2025
PubMed
概括

一种新的方法,Tooth-ASAM,适应了分段任何模型 (SAM) 以实现精确的牙细分. 这种先进的技术可以改善牙科和手术的数字牙科工作流程.

关键词:
牙科 牙科是指牙科的专业.多模式图像多模式图像分段 任何 模型 模型牙细分是指牙的细分.

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科学领域:

  • 牙科 牙科是指牙科的专业.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 越来越多的患者数量和牙科的数字化转型需要针对各种应用进行精确的牙细分.
  • 目前的方法可能缺乏精度复杂的数字牙科工作流程,如正牙科,植入物手术和假牙.

研究的目的:

  • 适应分段任何模型 (SAM) 以实现高性能牙细分.
  • 引入一种新的方法,Tooth-ASAM,用于从多式牙科图像中精确细分牙.

主要方法:

  • 开发了基于适配器的图像编码器和面具解码器,以定制SAM用于牙细分.
  • 在各种数据集上评估了Tooth-ASAM方法,包括圆束计算机断层扫描 (CBCT),全景X射线和微型摄像头图像.

主要成果:

  • 牙-ASAM在所有四个评估数据集中都表现出惊人的表现,超过了最先进的方法.
  • 在关键的细分指标中取得了很好的结果,例如Dice系数,IoU,HD95和ASSD.
  • 与现有技术相比,提供了感知上更准确的细分结果.

结论:

  • 使用SAM与有效的适应训练策略,可以实现精确的牙细分.
  • 牙-ASAM方法显示了在数字牙科的临床应用的巨大潜力,包括正牙科,口腔植入术和假牙科.