Toward accurate tooth segmentation from computed tomography images using a hybrid level set model

Gan, Yangzhou; Xia, Zeyang; Xiong, Jing; Zhao, Qunfei; Hu, Ying; Zhang, Dongwen

doi:10.1118/1.4901521

Cited by 72 publications

(63 citation statements)

References 38 publications

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“…However, other in vitro teeth segmentation methods that have already been tested, provide much faster segmentation and are useful in clinical practice. [28][29][30] In conclusion, the null hypothesis was rejected. Compared with the other windowing protocols we investigated, the 1131/1858 windowing protocol allows for significantly closer 3D in vivo segmentation of a tooth from I-CAT CBCT, with excellent intra-observer repeatability and inter-observer reproducibility.…”

Section: Resultsmentioning

confidence: 92%

Validation of a windowing protocol for accurate in vivo tooth segmentation using i-CAT cone beam computed tomography

Rastegar

Thumilaire²,

Odri

et al. 2018

Adv Clin Exp Med

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Section: Resultsmentioning

confidence: 92%

Validation of a windowing protocol for accurate in vivo tooth segmentation using i-CAT cone beam computed tomography

Rastegar

Thumilaire²,

Odri

et al. 2018

Adv Clin Exp Med

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“…It starts from manually picking up a starting slice from tooth neck part and initializing each tooth in this slice. The initialization and segmentation of the starting slice is implemented using the method in [9].Then, crown segmentation is completed automatically slice-by-slice. In the segmentation of each slice, the Radon transform is applied to extract a line to separate neighboring crowns into independent ones.…”

Section: The Crown Segmentation Methodsmentioning

confidence: 99%

“…2 (a)-1. Fig.3 (a), (b) and (c) are the results of Hosntalab's [5], Gao's [6] and Gan's [9] tooth segmentation methods. The results indicate that these methods fail to extract the crown boundaries occluded by metal artifacts with high intensity and get poor results.…”

Section: Methodsmentioning

confidence: 99%

“…The results indicate that these methods fail to extract the crown boundaries occluded by metal artifacts with high intensity and get poor results. are the results using Hosntalab's [5], Gao's [6], and Gan's [9] methods, respectively. (d) is the manual segmentation results.…”

Section: Methodsmentioning

confidence: 99%

“…Yau et al [7] and Ji et al [8] used the similar model to segment tooth. Gan et al [9] developed a hybrid level set model for the accurate segmentation of root.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Crown Segmentation From Computed Tomography Images With Metal Artifacts

Xia

Gan

Xiong

et al. 2016

IEEE Signal Process. Lett.

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Tooth segmentation from dental computed tomography (CT) images with metal artifacts is challenging as metal artifacts make some of the crown boundaries unrecognizable. This letter proposes a semi-automatic method for crown segmentation from CT images with metal artifacts. A user manually selects a starting slice and initializes this slice. Then crown contours are segmented automatically from volumetric CT images slice-by-slice. In the segmentation of each slice, the Radon transform is used to extract a line to separate neighboring crowns into independent ones. A statistical shape prior based level set model is then applied to segment each crown from the mesial or distal side of the line. The proposed method was tested on 15 set of volumetric images. Experimental results validated that it is effective to extract crown contours from CT images with metal artifacts.Index Terms-Dental CT image, image segmentation, level set method, statistical shape prior model, metal artifacts.1070-9908 (c)

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Tooth instance segmentation based on capturing dependencies and receptive field adjustment in cone beam computed tomography

Dou

Gao

Mao

et al. 2022

Computer Animation & Virtual

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Automatic and accurate instance segmentation of teeth can provide important support for computer-aided orthodontic work. Traditional methods for tooth segmentation studies often ignore the rich structural features of teeth. Capturing the complete and accurate geometry as well as morphological details of a single tooth remains a challenge for current tooth segmentation studies. In this article, a new tooth segmentation deeplearning network based on capturing dependencies and receptive field adjustment in cone beam computed tomography (CBCT) is proposed to achieve automatic and accurate instance segmentation of dental CBCT data. The method acquires coarse-level features of tooth and accurate tooth centroids in the first stage, and acquires the instance information and spatial position localization of the tooth. The encoding process in the second stage of the network introduces a guidance module for obtaining tooth geometry information based on a 3D self-attention mechanism to capture dependencies in CBCT. The proposed tooth feature integration module is based on multiscale fusion of dilated convolutions to capture tooth detailed information at multiple scales, and the network receptive field was adjusted. Extensive evaluation, ablation, and comparison experiments demonstrate that our method exhibits state-of-the-art segmentation performance and accurate instance segmentation results, reflecting their potential applicability in clinical medicine.

show abstract

Toward accurate tooth segmentation from computed tomography images using a hybrid level set model

Abstract: The presented tooth segmentation method can be used to segment tooth contours from CT images accurately and efficiently.

Cited by 72 publications

References 38 publications

Validation of a windowing protocol for accurate in vivo tooth segmentation using i-CAT cone beam computed tomography

Validation of a windowing protocol for accurate in vivo tooth segmentation using i-CAT cone beam computed tomography

Crown Segmentation From Computed Tomography Images With Metal Artifacts

Tooth instance segmentation based on capturing dependencies and receptive field adjustment in cone beam computed tomography

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