Authors:
Tibor Kubík
1
and
Michal Španěl
1
;
2
Affiliations:
1
Department of Computer Graphics and Multimedia, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic
;
2
TESCAN 3DIM, Brno, Czech Republic
Keyword(s):
Landmark Detection in 3D, Polygonal Meshes, Multi-view Deep Neural Networks, RANSAC, U-Net, Heatmap Regression, Teeth Detection, Dental Scans.
Abstract:
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. What’s more, in terms of 3D data, the DNN processing comes with high memory and computational
time requirements, which do not meet the needs of clinical applications. We present a robust method for
tooth landmark detection based on a 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 accuracy, our method is robust to missing teeth, as it can
correctly detect the presence of teeth in 97.68% cases.
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