A Fast Segmentation Method for STL Teeth Model

Li, Zhanli; Ning, Xiaojuan; Zeng-bo, Wang

doi:10.1109/iccme.2007.4381713

Cited by 22 publications

(12 citation statements)

References 4 publications

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“…x×l+i,y×m+j,z×n+k (2) where β x,y,z is the downsampling coefficient, µ is the value at(x × l + i, y × m + j, z × n + k), (l,m,n) denotes strides in the (x,y,z). Different from local connection in convolution layer, the last layers of deep CNNs are usually fully connected, referred to as full-connected layers, which are used to connect classifier layers.…”

Section: ) Tooth Classification Network Based On Two-level Hierarchimentioning

confidence: 99%

“…The dental models play an important role in the clinical orthodontic diagnosis. They can truly show the 3D anatomy structure of patients with malocclusion, as well as the shape and position distribution of the teeth, and assist dentists to design an efficient and accurate dental treatment plan by extracting, moving and rearranging the teeth from the dental models [2], [3]. Therefore, tooth segmentation is a core step in many oral medical research processes and is the basis for computer-aided dental diagnosis and treatment.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, many segmentation methods based on the three-dimensional dental model have emerged. Li et al [2] proposed an interactive tooth segmentation method based on fast marching watersheds and threshold-controlling method. Wu et al [7] and Yuan et al [8] adopted morphologic skeleton operations to optimize the feature regions and identify the tooth boundaries, and achieved good segmentation results.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Classification and Segmentation of Teeth on 3D Dental Model Using Hierarchical Deep Learning Networks

et al. 2019

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To solve the problem of low efficiency, the complexity of the interactive operation, and the high degree of manual intervention in existing methods, we propose a novel approach based on the sparse voxel octree and 3D convolution neural networks (CNNs) for segmenting and classifying tooth types on the 3D dental models. First, the tooth classification method capitalized on the two-level hierarchical feature learning is proposed to solve the misclassification problem in highly similar tooth categories. Second, we exploit an improved three-level hierarchical segmentation method based on the deep convolution features to conduct segmentation of teeth-gingiva and inter-teeth, respectively, and the conditional random field model is used to refine the boundary of the gingival margin and the inter-teeth fusion region. The experimental results show that the classification accuracy in Level_1 network is 95.96%, the average classification accuracy in Level_2 network is 88.06%, and the accuracy of tooth segmentation is 89.81%. Compared with the existing state-of-the-art methods, the proposed method has higher accuracy and universality, and it has great application potential in the computer-assisted orthodontic treatment diagnosis.

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Section: ) Tooth Classification Network Based On Two-level Hierarchimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Classification and Segmentation of Teeth on 3D Dental Model Using Hierarchical Deep Learning Networks

et al. 2019

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“…[38] first proposes multiscale SSM to solve this problem. Several studies applying different multiscale analysis to SSM are proposed such as spherical wavelet on spherical topology [39], subdivision based surface wavelet [40] on spherical topology, and diffusion wavelet on arbitrary surface topology [41]. …”

Section: Statistical Modelsmentioning

confidence: 99%

3D segmentation of maxilla in cone-beam computed tomography imaging using base invariant wavelet active shape model on customized two-manifold topology

Chang

Xia²,

Yuan

et al. 2013

Journal of X-Ray Science and Technology

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Recent advances in cone-beam computed tomography (CBCT) have rapidly enabled widepsread applications of dentomaxillofacial imaging and orthodontic practices in the past decades due to its low radiation dose, high spatial resolution, and accessibility. However, low contrast resolution in CBCT image has become its major limitation in building skull models. Intensive hand-segmentation is usually required to reconstruct the skull models. One of the regions affected by this limitation the most is the thin bone images. This paper presents a novel segmentation approach based on wavelet density model (WDM) for a particular interest in the outer surface of anterior wall of maxilla. Nineteen CBCT datasets are used to conduct two experiments. This mode-based segmentation approach is validated and compared with three different segmentation approaches. The results show that the performance of this model-based segmentation approach is better than those of the other approaches. It can achieve 0.25 ± 0.2mm of surface error from ground truth of bone surface.

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“…According to the prior knowledge of tooth geometry, there are obvious negative curvature features at the tooth boundary. The curvature based methods [1,2,3,4,5,6] usually detect these negative curvature features and divide the surface into different parts. However, there are also negative curvature characteristics on the surface of teeth and gums, which can form noise and cause serious interference.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Song

et al. 2020

Graphical Models

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Automatic segmentation of 3D tooth models into individual teeth is an important step in orthodontic CAD systems. 3D tooth segmentation is a mesh instance segmentation task. Complex geometric features on the surface of 3D tooth models often lead to failure of tooth boundary detection, so it is difficult to achieve automatic and accurate segmentation by traditional mesh segmentation methods. We propose a novel solution to address this problem. We map a 3D tooth model isomorphically to a 2D harmonic parameter space and convert it into an image. This allows us to use a CNN to learn a highly robust image segmentation model to achieve automated and accurate segmentation of 3D tooth models. Finally, we map the image segmentation mask back to the 3D tooth model and refine the segmentation result using an improved Fuzzy-Clustering-and-Cuts algorithm. Our method has been incorporated into an orthodontic CAD system, and performs well in practice.

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A Fast Segmentation Method for STL Teeth Model

Cited by 22 publications

References 4 publications

Automatic Classification and Segmentation of Teeth on 3D Dental Model Using Hierarchical Deep Learning Networks

Automatic Classification and Segmentation of Teeth on 3D Dental Model Using Hierarchical Deep Learning Networks

3D segmentation of maxilla in cone-beam computed tomography imaging using base invariant wavelet active shape model on customized two-manifold topology

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

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