FaLdViT: A Simple Yet effective framework to detect Cephalometric landmarks

Cephalometric analysis plays a vital role in the domains of orthodontics, where it is consistently employed. The primary procedure involved in this analysis entails identifying craniofacial landmarks on lateral cephalograms. These landmarks yield diagnostic insights into a patient’s craniofacial state and influence the decisions made regarding treatment planning. Owing to the variability in anatomical structures among individuals and the quality of X-ray imaging, achieving precise and consistent identification of landmarks within high-resolution lateral cephalograms poses a considerable challenge. Automatically and accurately locating these landmarks is a challenging issue because X-ray images possess high resolution, and the markers exhibit distinct clustering characteristics on specific organs, Based on this observation, we present a new vision transformer based approach for accurate anatomical Facial Landmark detection named FaLdViT. The proposed method comprises a target detection model designed to differentiate between the regions of the ears, eyes, and mouth in X-ray images, along with a hybrid landmarks detection model. In our network model FaLdViT, within a precision range of 2.0mm, the model achieved 77.36% average accuracy for each landmark point. which is the acceptable precision range in clinical practice, and the code is about to be open-sourced.