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Learning Cephalometric Landmarks for Diagnostic Features Using Regression Trees. 2022

Sameera Suhail, and Kayla Harris, and Gaurav Sinha, and Maayan Schmidt, and Sujala Durgekar, and Shivam Mehta, and Madhur Upadhyay
Department of Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia.

Lateral cephalograms provide important information regarding dental, skeletal, and soft-tissue parameters that are critical for orthodontic diagnosis and treatment planning. Several machine learning methods have previously been used for the automated localization of diagnostically relevant landmarks on lateral cephalograms. In this study, we applied an ensemble of regression trees to solve this problem. We found that despite the limited size of manually labeled images, we can improve the performance of landmark detection by augmenting the training set using a battery of simple image transforms. We further demonstrated the calculation of second-order features encoding the relative locations of landmarks, which are diagnostically more important than individual landmarks.

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