Orthodontic Department, Hamad Dental Center, Hamad Medical Corporation, Doha, Qatar.
Scottish Craniofacial Research Group, Glasgow University Dental Hospital & School, School of Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom.
Eur J Orthod. 2024 Dec 1;46(6). doi: 10.1093/ejo/cjae056.
The facial landmark annotation of 3D facial images is crucial in clinical orthodontics and orthognathic surgeries for accurate diagnosis and treatment planning. While manual landmarking has traditionally been the gold standard, it is labour-intensive and prone to variability.
This study presents a framework for automated landmark detection in 3D facial images within a clinical context, using convolutional neural networks (CNNs), and it assesses its accuracy in comparison to that of ground-truth data.
Initially, an in-house dataset of 408 3D facial images, each annotated with 37 landmarks by an expert, was constructed. Subsequently, a 2.5D patch-based CNN architecture was trained using this dataset to detect the same set of landmarks automatically.
The developed CNN model demonstrated high accuracy, with an overall mean localization error of 0.83 ± 0.49 mm. The majority of the landmarks had low localization errors, with 95% exhibiting a mean error of less than 1 mm across all axes. Moreover, the method achieved a high success detection rate, with 88% of detections having an error below 1.5 mm and 94% below 2 mm.
The automated method used in this study demonstrated accuracy comparable to that achieved with manual annotations within clinical settings. In addition, the proposed framework for automatic landmark localization exhibited improved accuracy over existing models in the literature. Despite these advancements, it is important to acknowledge the limitations of this research, such as that it was based on a single-centre study and a single annotator. Future work should address computational time challenges to achieve further enhancements. This approach has significant potential to improve the efficiency and accuracy of orthodontic and orthognathic procedures.
3D 面部图像的面部地标标注在临床正畸和正颌手术中至关重要，可用于准确诊断和治疗计划。虽然传统上手动地标标注是金标准，但它劳动强度大且容易出现变化。
本研究提出了一种在临床环境中使用卷积神经网络（CNN）自动检测 3D 面部图像地标位置的框架，并评估其与真实数据相比的准确性。
首先，构建了一个包含 408 个 3D 面部图像的内部数据集，每个图像都由专家标注了 37 个地标。随后，使用该数据集训练了一个 2.5D 基于补丁的 CNN 架构，以自动检测相同的地标集。
开发的 CNN 模型表现出很高的准确性，总体平均定位误差为 0.83±0.49 毫米。大多数地标具有较低的定位误差，95%的地标在所有轴上的平均误差小于 1 毫米。此外，该方法的检测成功率很高，88%的检测误差小于 1.5 毫米，94%的检测误差小于 2 毫米。
本研究中使用的自动方法在临床环境中与手动标注的准确性相当。此外，与文献中现有的模型相比，所提出的自动地标定位框架表现出更高的准确性。尽管取得了这些进展，但需要认识到这项研究的局限性，例如它基于单中心研究和单个标注员。未来的工作应解决计算时间挑战，以实现进一步的改进。这种方法具有提高正畸和正颌手术效率和准确性的巨大潜力。
