Quantitative cephalometric analysis is a standard clinical and research tool in modern orthodontics which plays an integral role in orthodontic diagnosis, maxillofacial surgery, and treatment planning. The accurate identification and reproducible localization of cephalometric landmarks allows the quantification and classification of anatomical abnormalities. The traditional manual way of marking cephalometric landmarks on lateral cephalograms is a very time-consuming job and is miles hard to achieve stable detection accuracy because of uneven experience of orthodontists. Endeavors to develop automated landmark detection systems have persistently been made but they are inadequate for orthodontic applications because of low reliability of specific landmarks. To facilitate the development of robust AI solutions for quantitative morphometric analysis, we organized an Automatic Cephalometric Landmark Detection Challenge that will assist researchers to develop an appropriate automated landmark detection framework which can provide clinical assistance as a computer-aided analysis tool and make contributions to better cephalometric decisions.
In this repository, we provide the code to help participants get started on their algorithm development for the CEPHA29 challenge.