The accurate identification and precise localization of cephalometric landmarks enable the classification and quantification of anatomical abnormalities. The traditional way of marking cephalometric landmarks on lateral cephalograms is a monotonous and time-consuming job. Endeavours to develop automated landmark detection systems have persistently been made, however, they are inadequate for orthodontic applications due to unavailability of a reliable dataset. We proposed a new state-of-the-art dataset to facilitate the development of robust AI solutions for quantitative morphometric analysis. The dataset includes 1000 lateral cephalometric radiographs (LCRs) obtained from 7 different radiographic imaging devices with varying resolutions, making it the most diverse and comprehensive cephalometric dataset to date. The clinical experts of our team meticulously annotated each radiograph with 29 cephalometric landmarks, including the most significant soft tissue landmarks ever marked in any publicly available dataset. Additionally, our experts also labelled the cervical vertebral maturation (CVM) stage of the patient in a radiograph, making this dataset the first standard resource for CVM classification. We believe that this dataset will be instrumental in the development of reliable automated landmark detection frameworks for use in orthodontics and beyond.

翻译：头影测量标志点的准确识别与精确定位有助于解剖异常的分类与量化。在侧位头影图像上手动标记头影测量标志点的传统方式单调且耗时。尽管人们持续致力于开发自动标志点检测系统，但由于缺乏可靠数据集，这些系统仍难以满足正畸应用需求。我们提出一个新的先进数据集，以促进鲁棒人工智能解决方案在定量形态测量分析中的开发。该数据集包含来自7种不同分辨率放射成像设备的1000张侧位头影放射片（LCR），是目前最多样化且最全面的头影测量数据集。团队临床专家对每张放射片进行了29个头影测量标志点的精细标注，涵盖迄今为止任何公开数据集中最显著的软组织标志点。此外，专家们还标注了放射片中患者的颈椎骨成熟度（CVM）阶段，使该数据集成为首个用于CVM分类的标准资源。我们相信，该数据集将有助于开发可靠的正畸及更多领域自动标志点检测框架。