Cephalometric Landmark Detection across Ages with Prototypical Network

TL;DR

This paper introduces CeLDA, a novel prototypical network-based method for accurate cephalometric landmark detection across different age groups, addressing the challenge of appearance variation between adolescents and adults.

Contribution

It presents the first unified approach and dataset for cephalometric landmark detection across ages, with strategies for prototype alignment and relation mining.

Findings

CeLDA outperforms existing methods on both adolescent and adult datasets.

The proposed strategies improve landmark prototype alignment across age groups.

The method achieves robust detection despite appearance discrepancies.

Abstract

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