ToothAxis: Generalizable Tooth Axis Estimation Network from CBCT or IOS Models.

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Nan Bao, Qingyao Luo, Jiamin Wu, Zhiming Cui, Yue Zhao
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引用次数: 0

Abstract

Tooth axes, indicating the orientation of teeth, are crucial in orthodontics and dental implants. The precise and automated estimation of tooth axes in 3D dental models is of significant importance. In clinical settings, Cone-beam computed tomography (CBCT) images and intraoral scanning (IOS) models are the two primary forms of digital data, providing 3D volumetric and surface information of the oral cavity, respectively. However, the detection of tooth axes remains largely manual annotation due to the complexities associated with geometric definitions and the variations among different tooth types and individuals. In this paper, we propose a novel two-stage network, named ToothAxis, for tooth axis estimation using either CBCT or IOS models. Given that IOS models only capture the tooth crown surface and lack information about the tooth roots, we initially employ an implicit-function tooth completion module for 3D tooth completion in the first stage. Subsequently, with the 3D tooth models segmented from CBCT images or completed from IOS models, a point-wise offset-based module is proposed in the second stage to accurately estimate the tooth axes. This design aims to encode tooth orientation into a dense representation, which is better suited for sparse information regression tasks, such as tooth axis estimation. Additionally, we incorporate a class-specific feature attention module to integrate global context representation, thereby enhancing robustness in managing diverse tooth shapes. We evaluated ToothAxis on a dataset obtained from real-world dental clinics, comprising 529 tooth models with corresponding CBCT images and paired IOS models. Finally, the ToothAxis achieves angle errors of LA ($2.921^{\circ }$), PSA ($4.801^{\circ }$), and LSA ($5.074^{\circ }$) on tooth models extracted from CBCT images, and LA ($5.326^{\circ }$), PSA ($6.360^{\circ }$), and LSA ($6.520^{\circ }$) on partial crowns extracted from IOS models. Extensive evaluations, ablation studies, and comparative analyses demonstrate that our method achieves accurate tooth axis estimations and surpasses state-of-the-art approaches.

牙轴:基于CBCT或IOS模型的可推广的牙轴估计网络。
显示牙齿方向的牙轴在正畸和种植牙中是至关重要的。在三维牙齿模型中,牙轴的精确自动估计具有重要意义。在临床环境中,锥形束计算机断层扫描(CBCT)图像和口内扫描(IOS)模型是两种主要的数字数据形式,分别提供口腔的三维体积和表面信息。然而,由于与几何定义相关的复杂性以及不同牙齿类型和个体之间的差异,牙齿轴的检测仍然主要是手工注释。在本文中,我们提出了一种新的两阶段网络,称为牙轴,用于使用CBCT或IOS模型进行牙轴估计。考虑到IOS模型只捕获牙冠表面,缺乏关于牙根的信息,我们最初在第一阶段采用隐式功能牙齿补全模块进行3D牙齿补全。随后,利用CBCT图像分割或IOS模型完成三维牙齿模型,在第二阶段提出基于点向偏移的模块来准确估计牙齿轴。本设计旨在将牙齿方向编码为密集表示,更适合稀疏信息回归任务,如牙齿轴估计。此外,我们结合了一个特定类别的特征注意模块来整合全局上下文表示,从而增强了管理不同牙齿形状的稳健性。我们在一个来自真实牙科诊所的数据集上对ToothAxis进行了评估,该数据集包括529个具有相应CBCT图像和配对IOS模型的牙齿模型。最后,tooth axis在CBCT图像提取的牙齿模型上实现了LA ($2.921^{\circ}$)、PSA ($4.801^{\circ}$)和LSA ($5.074^{\circ}$)的角度误差,在IOS模型提取的部分冠上实现了LA ($5.326^{\circ}$)、PSA ($6.360^{\circ}$)和LSA ($6.520^{\circ}$)的角度误差。广泛的评估、消融研究和比较分析表明,我们的方法可以实现准确的牙轴估计,并且超越了最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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