Robust Teeth Detection in 3D Dental Scans by Automated Multi-View Landmarking

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU144344" target="_blank" >RIV/00216305:26230/22:PU144344 - isvavai.cz</a>
Result on the web
<a href="https://www.scitepress.org/PublicationsDetail.aspx?ID=6XIfWnl5LKU=&t=1" target="_blank" >https://www.scitepress.org/PublicationsDetail.aspx?ID=6XIfWnl5LKU=&t=1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0010770700003123" target="_blank" >10.5220/0010770700003123</a>

Result language
angličtina
Original language name
Robust Teeth Detection in 3D Dental Scans by Automated Multi-View Landmarking
Original language description
Landmark detection is frequently an intermediate step in medical data analysis. More and more often, these data are represented in the form of 3D models. An example is a 3D intraoral scan of dentition used in orthodontics, where landmarking is notably challenging due to malocclusion, teeth shift, and frequent teeth missing. Whats more, in terms of 3D data, the DNN processing comes with high requirements for memory and computational time, which do not meet the needs of clinical applications. We present a robust method for tooth landmark detection based on the multi-view approach, which transforms the task into a 2D domain, where the suggested network detects landmarks by heatmap regression from several viewpoints. Additionally, we propose a post-processing based on Multi-view Confidence and Maximum Heatmap Activation Confidence, which can robustly determine whether a tooth is missing or not. Experiments have shown that the combination of Attention U-Net, 100 viewpoints, and RANSAC consensus method is able to detect landmarks with an error of 0:75 0:96 mm. In addition to the promising accuracies, our method is robust to missing teeth, as it can correctly detect the presence of teeth in 97.68% cases.
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022)
ISBN
978-989-758-552-4
ISSN
—
e-ISSN
—
Number of pages
11
Pages from-to
24-34
Publisher name
Institute for Systems and Technologies of Information, Control and Communication
Place of publication
Vienna
Event location
Wien, Austria
Event date
Feb 9, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000778898600002

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