Automatic Landmark Identification on IntraOralScans

地标 计算机科学 人工智能 计算机视觉 鉴定(生物学) 模式识别(心理学) 植物 生物
作者
Baptiste Baquero,Maxime Gillot,Lucía Cevidanes,Najla Al Turkestani,Marcela Gurgel,Mathieu Leclercq,Jonas Bianchi,Marília Yatabe,Antônio Carlos de Oliveira Ruellas,Camila Massaro,Aron Aliaga,Maria Antonia Alvarez Castrillon,Diego Rey,Juan Fernando Aristizábal,Juan Carlos Prieto
出处
期刊:Lecture Notes in Computer Science 卷期号:: 32-42 被引量:1
标识
DOI:10.1007/978-3-031-23179-7_4
摘要

With the advent of 3D printing and additive manufacturing of dental devices, IntraOral scanners (IOS) have gained wide adoption in dental practices and allowed for efficient workflows in clinical settings. Accurate automatic identification of dental landmarks in IOS is required to aid dental researchers and clinicians to plan and assess tooth position for crown restorations, orthodontics movements, and/or implant dentistry. In this paper, we present a new algorithm for Automatic Landmark Identification on IntraOralScans (ALIIOS), that combines image processing, image segmentation, and machine learning approaches to automatically and accurately identify commonly used landmarks on IOSs. Four hundred and five digital dental models were pre-processed by 3 clinician experts to manually annotate 5 landmarks on each dental crown in the upper and lower arches. Our approach uses the PyTorch3D rendering engine to capture 2D views of the dental arches from different viewpoints as well as the target 3D patches at the location of the landmarks. The ALIIOS algorithm synthesizes these 3D patches with a U-Net and allows accurate placement of the landmarks on the surface of each dental crown. Our results, after cross-validation, show an average distance error between the prediction and the clinicians' landmarks of 0.43 ± 0.28 mm and 0.45 ± 0.28 mm for respectively lower and upper occlusal landmarks, and 0.62 ± 0.28 mm for lower and upper cervical landmarks. There was on average a 5% error of landmarks more than 1.5 mm away from the clinicians' landmarks, due to errors in landmark nomenclature or improper segmentation. In conclusion, we present and validate a novel algorithm for accurate automated landmark identification on intraoral scans to increase efficiency and facilitate quantitative assessments in clinical practice.
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