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一个基于深度学习的开放框架和模型,用于牙科CBCT中的牙实例细分

You Zhou¹, Yan Xu², Basel Khalil¹

¹Periodontology & Implant Dentistry, Faculty of Dentistry, The Prince Philip Dental Hospital, The University of Hong Kong, 34 Hospital Road, Sai Ying Pun, Hong Kong.

Clinical oral investigations

|September 25, 2025

概括

OraSeg是一个新的深度学习工具,为35个口腔结构提供准确的,一键的牙科CBCT细分. 这种可访问的解决方案有助于牙医和研究人员进行诊断和采用数字牙科.

科学领域:

口腔和面放射学口腔和面放射学
人工智能在医学中的应用
医学图像分析医学图像分析

背景情况:

目前的圆束计算机断层扫描 (CBCT) 分段工具往往缺乏准确性,可访问性和全面的解剖覆盖.
牙科CBCT的精确细分对于诊断,治疗计划和研究至关重要.

研究的目的:

开发和验证OraSeg,一个深度学习模型,用于精确的牙CBCT图像的牙级实例细分.
为非专家用户创建一个用户友好的,一键工具,提高临床和研究应用的可访问性.

主要方法:

构建一个密集注释的牙科CBCT数据集,涵盖35个关键的口腔解剖结构.
使用UNetR与Swin变压器和空间Mamba用于多尺度特征融合的OraSeg模型的开发.
将OraSeg集成到3D Slicer平台中,提供一键图形用户界面.

主要成果:

对于CBCT实例细分,OraSeg实现了0.8316 ± 0.0305的Dice相似系数,超过了SwinUNETR和3D U-Net.
该模型在细分复杂的解剖区域 (如顶区域,膜骨边缘和下神经通道) 中表现出显著的性能改善.

结论:

OraSeg提供了一种有效且易于使用的解决方案,例如对牙科CBCT图像进行细分.
该工具使没有人工智能专业知识的牙医和研究人员能够进行一键细分,支持临床诊断,教育和研究.
通过用户友好的细分,OraSeg促进了数字牙科和精密医学的整合.

关键词:

3D切片机 3D切片机人工智能的人工智能是人工智能.形光束计算机断层扫描实例标签实例标签实例标签面成像面成像面成像

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