Automatic 3D tooth segmentation using convolutional neural networks in harmonic parameter space

Zhang, Jianda; Li, Chunpeng; Song, Qiang; Gao, Lin; Lai, Yuekun

doi:10.1016/j.gmod.2020.101071

Cited by 30 publications

(13 citation statements)

References 50 publications

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“…In general, they follow two main strategies. Some researchers proposed to convert the 3D scans into 2D images so that existing convolutional neural networks (CNNs) can be used for both teeth segmentation and classification [9]. This strategy may lead to acceptable results in the 2D space.…”

Section: Background and Summarymentioning

confidence: 99%

Teeth3DS: a benchmark for teeth segmentation and labeling from intra-oral 3D scans

Ben-Hamadou¹,

Smaoui²,

Chaabouni-Chouayakh³

et al. 2022

Preprint

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Teeth segmentation and labeling are critical components of Computer-Aided Dentistry (CAD) systems. Indeed, before any orthodontic or prosthetic treatment planning, a CAD system needs to first accurately segment and label each instance of teeth visible in the 3D dental scan, this is to avoid time-consuming manual adjustments by the dentist. Nevertheless, developing such an automated and accurate dental segmentation and labeling tool is very challenging, especially given the lack of publicly available datasets or benchmarks. This article introduces the first public benchmark, named Teeth3DS, which has been created in the frame of the 3DTeethSeg 2022 MICCAI challenge to boost the research field and inspire the 3D vision research community to work on intra-oral 3D scans analysis such as teeth identification, segmentation, labeling, 3D modeling and 3D reconstruction. Teeth3DS is made of 1800 intra-oral scans (23999 annotated teeth) collected from 900 patients covering the upper and lower jaws separately, acquired and validated by orthodontists/dental surgeons with more than 5 years of professional experience.

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Section: Background and Summarymentioning

confidence: 99%

Teeth3DS: a benchmark for teeth segmentation and labeling from intra-oral 3D scans

Ben-Hamadou¹,

Smaoui²,

Chaabouni-Chouayakh³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“… Lee et al (2020) established a novel method to estimate the average gray density level in the bone and tooth regions. Zhang et al (2020) developed CNN’s intuitive 3D tooth segmentation approach in harmonic parameter space. They built a 3D tooth model with 2D harmonic parameter space in tooth images and constructed the CNN to study how to perform high-quality and robust tooth segmentation automatically and precisely.…”

Section: Related Workmentioning

confidence: 99%

Clinical tooth segmentation based on local enhancement

Zhang

Yang

et al. 2022

Front. Mol. Biosci.

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The tooth arrangements of human beings are challenging to accurately observe when relying on dentists’ naked eyes, especially for dental caries in children, which is difficult to detect. Cone-beam computer tomography (CBCT) is used as an auxiliary method to measure patients’ teeth, including children. However, subjective and irreproducible manual measurements are required during this process, which wastes much time and energy for the dentists. Therefore, a fast and accurate tooth segmentation algorithm that can replace repeated calculations and annotations in manual segmentation has tremendous clinical significance. This study proposes a local contextual enhancement model for clinical dental CBCT images. The local enhancement model, which is more suitable for dental CBCT images, is proposed based on the analysis of the existing contextual models. Then, the local enhancement model is fused into an encoder–decoder framework for dental CBCT images. At last, extensive experiments are conducted to validate our method.

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“…Recently, deep learning-based methods have been popularly used to handle the task of tooth instance segmentation [30,38,39]. For example, Mask MCNet [38] proposed a framework that combines the Monte Carlo Convolutional Network (MCCNet) with Mask R-CNN to simultaneously locate each tooth object by predicting its bounding box and segment all the tooth points inside the box.…”

Section: D Tooth Understandingmentioning

confidence: 99%

“…Although recent learning-based methods have achieved impressive performance on 3D tooth instance segmentation [28,29,39], they rely heavily on a large number of data with dense manual annotations, such as labeling all points of every individual tooth from a dental model. Since annotating such training data is particularly time-consuming, it is hard to collect a large enough dataset to cover complex dental models in real-world, thus largely limiting the generalization of those learning-based segmentation methods [30,36,38].…”

Section: Introductionmentioning

confidence: 99%

DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentation

Qiu¹,

Ye²,

Chen³

et al. 2022

Preprint

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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 method to estimate the dental arch, in which the dental arch is initially generated by Bezier curve regression, and then a graph-based convolutional network (GCN) is trained to refine it. With the estimated dental arch, we then propose a novel Arch-aware Point Sampling (APS) method to assist the tooth centroid proposal generation. Meantime, a segmentor is independently trained using a patch-based training strategy, aiming to segment a tooth instance from a 3D patch centered at the tooth centroid. Experimental results on 4, 773 dental models have shown our DArch can accurately segment each tooth of a dental model, and its performance is superior to the state-of-the-art methods.

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Automatic 3D tooth segmentation using convolutional neural networks in harmonic parameter space

Cited by 30 publications

References 50 publications

Teeth3DS: a benchmark for teeth segmentation and labeling from intra-oral 3D scans

Teeth3DS: a benchmark for teeth segmentation and labeling from intra-oral 3D scans

Clinical tooth segmentation based on local enhancement

DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentation

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