DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentation
Liangdong Qiu, Chongjie Ye, Pei Chen, Yunbi Liu, Xiaoguang Han,, Shuguang Cui

TL;DR
DArch introduces a low-cost annotation approach for 3D tooth segmentation using dental arch priors, combining Bezier curve regression, GCN refinement, and patch-based segmentation to outperform existing methods.
Contribution
The paper presents a novel weakly supervised 3D tooth segmentation method leveraging dental arch priors, reducing annotation costs and improving accuracy.
Findings
Achieved superior segmentation accuracy over state-of-the-art methods.
Effectively utilized weak annotations for precise tooth instance segmentation.
Demonstrated robustness on a large dataset of 4,773 dental models.
Abstract
Automatic tooth instance segmentation on 3D dental models is a fundamental task for computer-aided orthodontic treatments. Existing learning-based methods rely heavily on expensive point-wise annotations. To alleviate this problem, we are the first to explore a low-cost annotation way for 3D tooth instance segmentation, i.e., labeling all tooth centroids and only a few teeth for each dental model. Regarding the challenge when only weak annotation is provided, we present a dental arch prior-assisted 3D tooth segmentation method, namely DArch. Our DArch consists of two stages, including tooth centroid detection and tooth instance segmentation. Accurately detecting the tooth centroids can help locate the individual tooth, thus benefiting the segmentation. Thus, our DArch proposes to leverage the dental arch prior to assist the detection. Specifically, we firstly propose a coarse-to-fine…
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Taxonomy
TopicsDental Radiography and Imaging · Endodontics and Root Canal Treatments · dental development and anomalies
