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Landmark localization and registration of 3D facial scans for the evaluation of orthodontic treatments in maxillofacial and oral surgery School of EECS: Prathap Nair, Dr Andrea Cavallaro School of Medicine and Dentistry: Dr Lifong Zou Mid-project update What is the problem? • To quantify 3D facial asymmetry • • • • Clinical diagnosis Treatment planning Post-treatment monitoring Statistical studies on a large population Approach: rigid registration • What is rigid registration? • Alignment of 2 or more faces • Classical approach: Iterative Closest Point (ICP) algorithm • Advantage • no prior info needed • Disadvantage • random points used for matching can lead to erroneous results Example Our approach • Rigid registration based on landmarks • Landmark detection via Statistical Shape Analysis BtG project: Achievement 1 • Improved accuracy Red – before BtG Green – after BtG Approach: overview Test Scan Reference scan Detection of Landmark Points Detection of Landmark Points Coarse registration using Key landmarks Detection of Stable regions Fine registration using the Semantic Regions Distance estimation Approach: overview Test Scan Reference scan Test scan Detection of Landmark Points Detection of Landmark Points Coarse registration using Key landmarks Detection of Stable regions Reference scan Fine registration using the Semantic Regions Distance estimation Approach: overview Test Scan Reference scan Test scan Detection of Landmark Points Detection of Landmark Points Coarse registration using Key landmarks Detection of Stable regions Reference scan Fine registration using the Semantic Regions Distance estimation Approach: overview Test Scan Reference scan Detection of Landmark Points Detection of Landmark Points Coarse registration using Key landmarks Test scan Detection of Stable regions Reference scan Key Landmarks Fine registration using the Semantic Regions Distance estimation Coarse registration Approach: overview Test Scan Reference scan Test scan Detection of Landmark Points Detection of Landmark Points Coarse registration using Key landmarks Detection of Stable regions Reference scan Fine registration using the Semantic Regions Distance estimation Approach: overview Test Scan Reference scan Test scan Detection of Landmark Points Detection of Landmark Points Coarse registration using Key landmarks Detection of Stable regions Reference scan Fine registration using the Semantic Regions Distance estimation Fine registration Approach: overview Test Scan Reference scan Detection of Landmark Points Detection of Landmark Points Coarse registration using Key landmarks Detection of Stable regions Fine registration using the Semantic Regions Distance estimation Example ICP Proposed approach BtG project: Achievement 2 • User friendly GUI • To ease burden on clinicians • User-feedback mechanisms Conclusions • Achievements • Improved landmark localisation accuracy • More user-friendly GUI with the user feedback • Current work • Clinical evaluation of the landmark detection accuracy • Validation of 3D facial scan registration accuracy • Further improving the GUI based on clinician feedback Contact: [email protected] [email protected]