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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022)

ISBN

978-989-758-552-4

ISSN

—

e-ISSN

—

Počet stran výsledku

11

Strana od-do

24-34

Název nakladatele

Institute for Systems and Technologies of Information, Control and Communication

Místo vydání

Vienna

Místo konání akce

Wien, Austria

Datum konání akce

9. 2. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000778898600002