Logo MachVis

Automated Landmark Detection for Cephalometric Radiographs Analysis

Automated Landmark Detection for Cephalometric Radiographs Analysis


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 professionalism of orthodontists. Endeavors to develop automated landmark detection systems have persistently been made but they are inadequate for clinical orthodontic applications because of low reliability of specific landmarks. In this project, we propose a novel framework for automatic and efficient localization of cephalometric landmarks with confidence regions using Bayesian Convolutional Neural Networks (BCNNs).
  • Dr Muhammad Moazam Faraz

Students

  • Muhammad Anwaar Khalid