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3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks

机译:3D使用深卷积神经网络的牙齿分割和标记

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摘要

In this paper, we present a novel approach for 3D dental model segmentation via deep Convolutional Neural Networks (CNNs). Traditional geometry-based methods tend to receive undesirable results due to the complex appearance of human teeth (e.g., missing/rotten teeth, feature-less regions, crowding teeth, extra medical attachments, etc.). Furthermore, labeling of individual tooth is hardly enabled in traditional tooth segmentation methods. To address these issues, we propose to learn a generic and robust segmentation model by exploiting deep Neural Networks, namely NNs. The segmentation task is achieved by labeling each mesh face. We extract a set of geometry features as face feature representations. In the training step, the network is fed with those features, and produces a probability vector, of which each element indicates the probability a face belonging to the corresponding model part. To this end, we extensively experiment with various network structures, and eventually arrive at a 2-level hierarchical CNNs structure for tooth segmentation: one for teeth-gingiva labeling and the other for inter-teeth labeling. Further, we propose a novel boundary-aware tooth simplification method to significantly improve efficiency in the stage of feature extraction. After CNNs prediction, we do graph-based label optimization and further refine the boundary with an improved version of fuzzy clustering. The accuracy of our mesh labeling method exceeds that of the state-of-art geometry-based methods, reaching 99.06 percent measured by area which is directly applicable in orthodontic CAD systems. It is also robust to any possible foreign matters on model surface, e.g., air bubbles, dental accessories, and many more.
机译:在本文中,我们通过深卷积神经网络(CNNS)提出了一种用于3D牙科模型分割的新方法。由于人体牙齿的复杂外观(例如,缺失/腐烂的牙齿,特色区域,拥挤的牙齿,额外的牙齿,额外的医疗附件等),传统的基于几何的方法往往会接受不良结果。此外,在传统的齿分割方法中几乎没有使单个牙齿的标记。为了解决这些问题,我们建议通过利用深神经网络,即NNS来学习通用和强大的分段模型。分割任务是通过标记每个网格面来实现的。我们将一组几何特征作为面部特征表示。在训练步骤中,网络被馈送与那些特征,并产生概率向量,其中每个元件指示属于相应模型部分的概率。为此,我们广泛使用各种网络结构进行实验,并最终到达2级分层CNNS结构,用于牙齿分割:一个用于牙齿 - 牙龈标记,另一个用于齿际标签。此外,我们提出了一种新的边界意识牙齿简化方法,以显着提高特征提取阶段的效率。在CNN预测之后,我们执行基于图形的标签优化,并通过改进的模糊聚类版本进一步改进边界。我们的网格标记方法的准确性超过了最先进的基于几何的方法,达到了99.06%的面积,该面积直接适用于正畸CAD系统。对于模型表面的任何可能的异物,例如,气泡,牙科配件等等,它也是强大的。

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  • 作者单位

    Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai 200050 Peoples R China|ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China|Univ Chinese Acad Sci Beijing 100049 Peoples R China;

    Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai 200050 Peoples R China|ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China|Univ Chinese Acad Sci Beijing 100049 Peoples R China;

    Zhejiang Univ State Key Lab CAD&CG Hangzhou 310058 Zhejiang Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Boundary-aware simplification; 3D mesh segmentation; deep convolutional neural networks; fuzzy clustering;

    机译:边界意识简化;3D网格分割;深卷积神经网络;模糊聚类;
  • 入库时间 2022-08-18 20:57:32

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