[1]刘沐然,谭敏慧,张煜.基于深度学习对 CBCT 图像采用多视角投影的牙齿分类[J].中国医学物理学杂志,2025,42(3):313-319.[doi:10.3969/j.issn.1005-202X.2025.03.005]
　LIU Muran,TAN Minhui,et al.Classification of teeth in CBCT images using deep learning with multi-view projection[J].Chinese Journal of Medical Physics,2025,42(3):313-319.[doi:10.3969/j.issn.1005-202X.2025.03.005]
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基于深度学习对 CBCT 图像采用多视角投影的牙齿分类()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第3期
页码:
313-319
栏目:
医学影像物理
出版日期:
2025-03-20

文章信息/Info

Title:
Classification of teeth in CBCT images using deep learning with multi-view projection
文章编号:
1005-202X(2025)03-0313-07
作者:
刘沐然 1; 2; 谭敏慧 2; 张煜 1
1. 南方医科大学生物医学工程学院,广东 广州 510515;2. 上海科技大学生物医学工程学院,上海 201210
Author(s):
LIU Muran1;  2;  TAN Minhui2;  ZHANG Yu1
1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; 2. School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
关键词:
深度学习; 锥形束计算机断层扫描; 牙齿识别; 多视角投影
Keywords:
deep learning;  cone beam computed tomography;  tooth identification;  multi-view projection
分类号:
R318
DOI:
10.3969/j.issn.1005-202X.2025.03.005
文献标志码:
A
摘要:
目的:针对当前基于锥形束计算机断层扫描(CBCT)图像的牙齿分类方法过于依赖精确分割,缺少对牙齿形态和位 置信息的综合利用,提出一种基于多视角投影和 Transformer 架构的牙齿分类方法,可对全年龄层的 CBCT 图像中的牙齿 (包括儿童病例)进行准确的 52 分类。方法:通过引入多视角投影,结合 Transformer 架构,融合语义分割和实例分割,由粗 至细进行牙齿分类任务,增强对牙齿空间位置信息的利用。采用国际牙科联盟(FDI)两位数牙位标记法对 CBCT 图像中 的牙齿进行分类,并对多视角融合效果进行评估。结果:改进后的方法能够准确区分恒牙与乳牙,同时有效地进行牙齿编 号,牙齿层面的分类准确率达到 0.982。结论:基于多视角投影与 Transformer 架构的牙齿分类方法实现对牙齿类别与位 置信息的有效融合,提高牙齿分类的精度,为个性化治疗方案的制定提供更为精确的基础。
Abstract:
Objective To address the issue that current methods for classifying teeth in cone beam computed tomography (CBCT) images overly rely on precise segmentation and lack utilization of tooth morphology and positional information, a tooth classification method based on multi-view projection and Transformer architecture is proposed for accurately classifying teeth from CBCT images across all age groups, including pediatric cases, into 52 categories. Methods The coarseto-fine tooth classification task was accomplished after enhancing the utilization of spatial positional information of the teeth by incorporating multi-view projection, integrating Transformer architecture, and combining semantic segmentation with instance segmentation. The two-digit notation system of the Federation Dentaire Internationale was adopted for classifying the teeth in CBCT images, and evaluating the effectiveness of multi-view fusion. Results The improved method effectively classified and numbered both permanent and deciduous teeth, achieving a tooth-level classification accuracy of 0.982. Conclusion The tooth classification method based on multi-view projection and Transformer architecture successfully integrates tooth category and positional information, and improves the accuracies of tooth classification and numbering, providing a more precise foundation for the formulation of personalized treatment schemes.

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备注/Memo

备注/Memo:
【收 稿 日 期】2024-11-12 【基 金 项 目】国 家 自 然 科 学 基 金(82472056);广 东 省 自 然 科 学 基 金 (2024A1515012004) 【作者简介】刘沐然,硕士研究生,研究方向:生物医学成像与图像处 理,E-mail: liumuran2022@163.com 【通信作者】张煜,博士,教授,研究方向:生物医学工程、医学影像智能 处理与分析,E-mail: yuzhang@smu.edu.cn
更新日期/Last Update: 2025-03-27