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Learning Cephalometric Landmarks for Diagnostic Features Using Regression Trees Suhail, Sameera; Harris, Kayla; Sinha, Gaurav; Schmidt, Maayan; Durgekar, Sujala; Mehta, Shivam; Upadhyay, Madhur

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.

Item Metadata

Title
Learning Cephalometric Landmarks for Diagnostic Features Using Regression Trees
Creator
Suhail, Sameera; Harris, Kayla; Sinha, Gaurav; Schmidt, Maayan; Durgekar, Sujala; Mehta, Shivam; Upadhyay, Madhur
Publisher
Multidisciplinary Digital Publishing Institute
Date Issued
2022-10-27
Description
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.
Subject
cephalograms; anatomical landmarks; machine learning; regression trees; orthodontics
Genre
Article
Type
Text
Language
eng
Date Available
2025-01-20
Provider
Vancouver : University of British Columbia Library
Rights
CC BY 4.0
DOI
10.14288/1.0447780
URI
http://hdl.handle.net/2429/90152
Affiliation
Science, Irving K. Barber Faculty of (Okanagan); Alumni; Non UBC; Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
Citation
Bioengineering 9 (11): 617 (2022)
Publisher DOI
10.3390/bioengineering9110617
Peer Review Status
Reviewed
Scholarly Level
Faculty; Researcher
Rights URI
https://creativecommons.org/licenses/by/4.0/
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CC BY 4.0