Multi-Stage Framework for Automated Cephalometric Landmark Detection

A Multi-Stage Framework for Automated Cephalometric Landmark Detection: Addressing Resolution and Spatial Invariance

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. Manual method of marking cephalometric landmarks on lateral cephalograms is a very time-consuming job and also results in inconsistent results due to human error. Endeavors to develop automated landmark detection systems have previously been worked on but they are inadequate for clinical orthodontic applications because of low reliability of specific landmarks.

This research proposed a novel multi-stage regression framework for automatic cephalometric landmark detection, which demonstrated significant improvement over traditional methods. The proposed framework is based on a two-stage approach that uses a multi-head CNN architecture to achieve superior performance. The shared backbone of the network enables inter-module communication, which helps the modules learn from each other’s predictions and align themselves accordingly. This approach is especially valuable in clinical settings where accurate and efficient cephalometric landmark detection is crucial for diagnosis and treatment of various craniofacial abnormalities.

Presenter: Reeha Khan