  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
https://github.com/ShanghaiTech-IMPACT/Cephalometric-Landmark-Detection-across-Ages-with-Prototypical-Network
