Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs by a single observer. The automated landmarking workflow involved two successive DiffusionNet models and additional algorithms for facial segmentation. The dataset was randomly divided into a training and test dataset. The training dataset was used to train the deep learning networks, whereas the test dataset was used to evaluate the performance of the automated workflow. The precision of the workflow was evaluated by calculating the Euclidean distances between the automated and manual landmarks and compared to the intra-observer and inter-observer variability of manual annotation and the semi-automated landmarking method. The workflow was successful in 98.6% of all test cases. The deep learning-based landmarking method achieved precise and consistent landmark annotation. The mean precision of 1.69 (+/-1.15) mm was comparable to the inter-observer variability (1.31 +/-0.91 mm) of manual annotation. The Euclidean distance between the automated and manual landmarks was within 2 mm in 69%. Automated landmark annotation on 3D photographs was achieved with the DiffusionNet-based approach. The proposed method allows quantitative analysis of large datasets and may be used in diagnosis, follow-up, and virtual surgical planning.

翻译：三维面部立体摄影测量可在不使用电离辐射的情况下提供颅面软组织的详细表征。尽管手动标注标志点目前是头影测量的金标准，但该过程耗时且易受人为误差影响。本研究旨在开发并评估一种基于深度学习的自动化头影测量标注方法。由一名观测者对2897张三维面部照片中的十个标志点进行手动标注。自动标志点标注流程包含两个串联的DiffusionNet模型及附加的面部分割算法。数据集被随机划分为训练集和测试集：训练集用于训练深度学习网络，测试集用于评估自动化流程的性能。通过计算自动标注与手动标注标志点之间的欧氏距离评估流程精度，并与手动标注的观察者内/观察者间变异性及半自动标注方法进行比较。该流程在98.6%的测试案例中成功运行。基于深度学习的标志点标注方法实现了精准且一致的标志点定位，平均精度为1.69(±1.15)毫米，与手动标注的观察者间变异性(1.31±0.91毫米)相当。69%的自动标注与手动标注标志点的欧氏距离误差在2毫米以内。基于DiffusionNet的方法实现了三维照片的自动标志点标注。该方法可支持大数据集的定量分析，并可用于诊断、随访及虚拟手术规划。