Publication Details
LMVSegRNN and Poseidon3D: Addressing Challenging Teeth Segmentation Cases in 3D Dental Surface Orthodontic Scans
dental scans, tooth segmentation, 3D mesh segmentation, Poseidon3D, Poseidon's
Teeth 3D, LMVSegRNN, orthodontic mesh segmentation dataset
The segmentation of teeth in 3D dental scans is difficult due to variations in
teeth shapes, misalignments, occlusions, or the present dental appliances.
Existing methods consistently adhere to geometric representations, omitting the
perceptual aspects of the inputs. In addition, current works often lack
evaluation on anatomically complex cases due to the unavailability of such
datasets. We present a projection-based approach towards accurate teeth
segmentation that operates in a detect-and-segment manner locally on each tooth
in a multi-view fashion. Information is spatially correlated via recurrent
units. We show that a projection-based framework can precisely segment teeth in
cases with anatomical anomalies with negligible information loss. It outperforms
point-based, edge-based, and Graph Cut-based geometric approaches, achieving an
average weighted IoU score of 0.971220.038 and a Hausdorff distance at 95
percentile of 0.490120.571 mm. We also release Poseidon's Teeth 3D (Poseidon3D),
a novel dataset of real orthodontic cases with various dental anomalies like
teeth crowding and missing teeth.
@article{BUT193275,
author="Tibor {Kubík} and Michal {Španěl}",
title="LMVSegRNN and Poseidon3D: Addressing Challenging Teeth Segmentation Cases in 3D Dental Surface Orthodontic Scans",
journal="Bioengineering",
year="2024",
volume="11",
number="10",
pages="1--18",
doi="10.3390/bioengineering11101014",
issn="2306-5354",
url="https://www.mdpi.com/2306-5354/11/10/1014"
}