FDTooth:用于检测窗口和脱落的口腔内照片和CBCT图像
Keyuan Liu1, Marawan Elbatel2, Guang Chu1
1Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
Scientific data
|June 14, 2025
概括
一个新的数据集,FDTooth,可以使用口内照片自动检测化和脱 (FD). 这种资源有助于在没有侵入性手术的情况下进行早期牙科诊断.
科学领域:
- 牙科诊断 牙科诊断 牙科诊断
- 医学成像医学成像
- 计算机视觉 计算机视觉
背景情况:
- 窗和脱落 (FD) 在牙科护理中构成重大挑战,影响口腔健康.
- 圆束计算断层扫描 (CBCT) 可以提供精确的诊断,但受到时间和辐射暴露的限制.
- 缺乏结合口腔内照片和CBCT图像的公共数据集,阻碍了用于自动检测疾病的深度学习.
研究的目的:
- 引入FDTooth,这是一套用于开发深度学习模型的新型数据集,用于自动化窗口和隐藏检测.
- 为跨学科牙科诊断研究提供一个有价值的资源.
- 为了促进牙科疾病的非侵入性,高效的早期查.
主要方法:
- 创建FDTooth数据集,包括241名患者 (9-55岁) 的口内照片和CBCT图像.
- 在口腔内摄影的1800个边界框的注释与CBCT的地面真相.
- 开发一个基线深度学习模型,用于自动化FD检测.
主要成果:
- FDTooth数据集包括配对的口内照片和CBCT图像,并附有详细的注释.
- 成功开发了一种基线模型,用于从口内照片中自动检测FD.
- 数据集和模型作为推进自动牙科诊断的基础资源.
结论:
- FDTooth解决了公共数据集的关键需求,该数据集将口内图像和CBCT数据结合起来.
- 开发的数据集和基线模型支持对自动检测窗体和脱光的研究.
- 这项工作促进了早期牙科查和诊断的非侵入性和高效方法.
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