卷积神经网络辅助三维头影解剖标志自动化目标检测的研究与应用进展  

Advances in research and application of convolutional neural network-assisted automated target detection of anatomical landmarks in three-dimensional cephalograms

作　　者：庄文博 胡越 陈沁豪 商莉 张逸天 桂海军[3] Zhuang Wenbo;Hu Yue;Chen Qinhao;Shang Li;Zhang Yitian;Gui Haijun(Shanghai Jiao Tong University School of Medicine,Shanghai 200025,China;School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;Department of Oral and Cranio-Maxillofacial Surgery,Shanghai Ninth People's Hospital,College of Stomatology,Shanghai Jiao Tong University,National Center for Stomatology,National Clinical Research Center for Oral Diseases,Shanghai Key Laboratory of Stomatology,Shanghai Research Institute of Stomatology,Shanghai 200011,China)

机构地区：[1]上海交通大学医学院,上海200025 [2]上海交通大学电子信息与电气工程学院,上海200240 [3]上海交通大学医学院附属第九人民医院口腔颅颌面科,上海交通大学口腔医学院,国家口腔医学中心,国家口腔疾病临床医学研究中心,上海市口腔医学重点实验室,上海市口腔医学研究所,上海200011

出　　处：《中华口腔医学研究杂志(电子版)》2025年第1期70-74,共5页Chinese Journal of Stomatological Research(Electronic Edition)

基　　金：上海交通大学医学院大学生创新训练计划(1723X012);上海交通大学医学院附属第九人民医院研究型医师培育项目(2022hbyjxys-ghj)。

摘　　要：基于二维影像的头影测量分析技术是当前临床诊断的“金标准”,但仍存在由于二维图像易失真、解剖标志重叠等导致的“解剖误差”和人工手动标点造成的“人工误差”。三维头影测量分析目前已广泛应用于临床诊断,对于“解剖误差”有着重要辅助作用;自动化头影测量分析是利用图像处理与深度学习对头影解剖标志进行自动识别与标点,对于改善二维测量分析的“人工误差”有重要意义。卷积神经网络(CNN)是目前基于深度学习最有效的图像处理与目标检测方法,在三维头影解剖标志自动化目标测量领域有着巨大应用潜力。本文综合国内外有关研究与应用,回顾了三维头影解剖标志测量分析体系的现状,并对基于CNN的牙颌面三维头影解剖标志自动化目标检测的研究进展进行了探索与综述。Cephalometric analysis technology based on two-dimensional images has always been the‘golden standard’.Still,there are the problems of‘anatomical errors’caused by the distortion of two-dimensional images and overlapping of anatomical landmarks,and‘artificial errors’caused by manual punctuation.Three-dimensional(3D)cephalometric analysis,which has been widely used in clinical diagnosis,and playing a more and more important role for resolving the‘anatomical error’problem.Automatic cephalometric analysis,which uses image processing and deep learning for identifying and punctuating cephalometric landmarks automatically,could be used for resolving the‘manual error’problem.Convolutional neural network(CNN)based on deep learning is currently the most effective technology of image processing and target detection,which has shown its great potential for automatic target detection of 3D cephalometric landmarks.Based on literature review,we summarized the current status of 3D cephalometric analysis and the research progress of CNN for automatic target detection of 3D cephalometric landmarks.

关 键 词：卷积神经网络 牙颌面畸形 头影测量分析 三维解剖标志 深度学习 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]