Learning Cephalometric Landmarks for Diagnostic Features Using Regression Trees. Academic Article

abstract

• 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.

authors

• Mehta, Shivam

published proceedings

• Bioengineering (Basel)

author list (cited authors)

• Suhail, S., Harris, K., Sinha, G., Schmidt, M., Durgekar, S., Mehta, S., & Upadhyay, M.

citation count

• 2

complete list of authors

• Suhail, Sameera||Harris, Kayla||Sinha, Gaurav||Schmidt, Maayan||Durgekar, Sujala||Mehta, Shivam||Upadhyay, Madhur

publication date

• October 2022

publisher

• MDPI  Publisher

keywords

• Anatomical Landmarks
• Cephalograms
• Machine Learning
• Orthodontics
• Regression Trees

Digital Object Identifier (DOI)

• 10.3390/bioengineering9110617

start page

• 617

end page

• 617

volume

• 9

issue

• 11

URL

• http://dx.doi.org/10.3390/bioengineering9110617