3D Lab Radboudumc, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands.
Department of Oral and Maxillofacial Surgery, Amsterdam University Medical Center (UMC), AMC, Academic Center for Dentistry Amsterdam (ACTA), Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
Sci Rep. 2024 Mar 18;14(1):6463. doi: 10.1038/s41598-024-56956-9.
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. The automated landmarking workflow involved two successive DiffusionNet models. The dataset was randomly divided into a training and test dataset. 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 a 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. 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.
三维面部体层摄影术提供了无电离辐射的颅面软组织的详细表现。虽然手动地标注释是头影测量分析的当前金标准，但它是一个耗时的过程，并且容易出现人为错误。本研究旨在开发和评估一种使用基于深度学习的方法进行自动头影测量标注的方法。在 2897 张 3D 面部照片上手动标注了 10 个地标。自动地标标注工作流程涉及两个连续的 DiffusionNet 模型。数据集随机分为训练数据集和测试数据集。通过计算自动地标和手动地标之间的欧几里得距离，并与手动标注的观察者内和观察者间变异性以及半自动地标标注方法进行比较，来评估工作流程的精度。该工作流程在所有测试案例中的成功率为 98.6%。基于深度学习的地标标注方法实现了精确且一致的地标标注。1.69±1.15 毫米的平均精度与手动标注的观察者间变异性（1.31±0.91 毫米）相当。基于 DiffusionNet 的方法实现了对 3D 照片的自动地标标注。所提出的方法允许对大型数据集进行定量分析，可用于诊断、随访和虚拟手术计划。
