Automated cephalometric landmark detection is crucial in real-world
orthodontic diagnosis. Current studies mainly focus on only adult subjects,
neglecting the clinically crucial scenario presented by adolescents whose
landmarks often exhibit significantly different appearances compared to adults.
Hence, an open question arises about how to develop a unified and effective
detection algorithm across various age groups, including adolescents and
adults. In this paper, we propose CeLDA, the first work for Cephalometric
Landmark Detection across Ages. Our method leverages a prototypical network for
landmark detection by comparing image features with landmark prototypes. To
tackle the appearance discrepancy of landmarks between age groups, we design
new strategies for CeLDA to improve prototype alignment and obtain a holistic
estimation of landmark prototypes from a large set of training images.
Moreover, a novel prototype relation mining paradigm is introduced to exploit
the anatomical relations between the landmark prototypes. Extensive experiments
validate the superiority of CeLDA in detecting cephalometric landmarks on both
adult and adolescent subjects. To our knowledge, this is the first effort
toward developing a unified solution and dataset for cephalometric landmark
detection across age groups. Our code and dataset will be made public on
this https URL

我们提出了 CeLDA 方法，通过将图像特征与标志物原型进行比较，利用原型网络进行标志物检测，解决了不同年龄组之间标志物出现差异的问题，并引入了新的策略改进原型对齐以获得全面的标志物估计。此外，我们还介绍了一种新的原型关系挖掘范式，用于利用标志物原型之间的解剖关系。大量实验证实了 CeLDA 在成年人和青少年标志物检测方面的优越性，这是首个针对不同年龄组的头影测量标志物检测的统一解决方案和数据集。