3D exemplar‐based random walks for tooth segmentation from cone‐beam computed tomography images

Pei, Yuru; Ai, Xingsheng; Zha, Hongbin; Xu, Tianmin; Ma, Gengyu

doi:10.1118/1.4960364

Cited by 18 publications

(5 citation statements)

References 48 publications

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“…Literature on Tooth Segmentation 1) Classical Methods: Classical image processing methods have been widely studied to achieve tooth segmentation [2], [3], [12], [13]. Several methods including region growing [1], watershed algorithm [2], [3], morphological operators [2], graph-cut-based segmentation [12], template-based registration [8], [9], and random forest classification [13] were implemented. Semiautomatic algorithms with manually annotated cues for easy implementation have also gained popularity [3], [14], [15].…”

Section: Related Workmentioning

confidence: 99%

“…Classical image processing methods that exploited region growing [1], morphological operations [2], and watershed algorithm [2], [3] were studied. Several works employed contour-based level-set methods [4]- [7] or shape-based registration methods [8], [9]. However, all the classical algorithms demonstrated limitations while handling the aforementioned challenging conditions, such as heterogeneous intensities, unclear boundaries, diverse anatomical poses, and presence of metal artifacts.…”

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confidence: 99%

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Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation

Chung

Lee

Hong

et al. 2020

Computers in Biology and Medicine

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Individual tooth segmentation from cone beam computed tomography (CBCT) images is an essential prerequisite for an anatomical understanding of orthodontic structures in several applications, such as tooth reformation planning and implant guide simulations. However, the presence of severe metal artifacts in CBCT images hinders the accurate segmentation of each individual tooth. In this study, we propose a neural network for pixel-wise labeling to exploit an instance segmentation framework that is robust to metal artifacts. Our method comprises of three steps: 1) image cropping and realignment by pose regressions, 2) metal-robust individual tooth detection, and 3) segmentation. We first extract the alignment information of the patient by pose regression neural networks to attain a volume-of-interest (VOI) region and realign the input image, which reduces the interoverlapping area between tooth bounding boxes. Then, individual tooth regions are localized within a VOI realigned image using a convolutional detector. We improved the accuracy of the detector by employing non-maximum suppression and multiclass classification metrics in the region proposal network. Finally, we apply a convolutional neural network (CNN) to perform individual tooth segmentation by converting the pixel-wise labeling task to a distance regression task. Metal-intensive image augmentation is also employed for a robust segmentation of metal artifacts. The result shows that our proposed method outperforms other state-of-the-art methods, especially for teeth with metal artifacts. Our method demonstrated 5.68% and 30.30% better accuracy in the F1 score and aggregated Jaccard index, respectively, when compared to the best performing state-of-the-art algorithms. The primary significance of the proposed method is two-fold: 1) an introduction of pose-aware VOI realignment followed by a robust tooth detection and 2) a metal-robust CNN framework for accurate tooth segmentation.Index Terms-Cone beam computed tomography image segmentation, pose-aware tooth detection, pose regression neural network, tooth instance segmentation.

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Section: Related Workmentioning

confidence: 99%

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confidence: 99%

Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation

Chung

Lee

Hong

et al. 2020

Computers in Biology and Medicine

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“…The random walk model, a direct identification method utilizing random numbers to determine the search direction, is also applied in tooth segmentation. Pei et al (2016) proposed a method that obtains the initial segmentation of teeth through a pure random walk approach. As an iterative refinement, they employ regularization through 3D exemplar registration and label propagation via random walks with soft constraints.…”

Section: Resultsmentioning

confidence: 99%

Semi or fully automatic tooth segmentation in CBCT images: a review

Zheng,

Gao,

Zhou

et al. 2024

PeerJ Computer Science

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Cone beam computed tomography (CBCT) is widely employed in modern dentistry, and tooth segmentation constitutes an integral part of the digital workflow based on these imaging data. Previous methodologies rely heavily on manual segmentation and are time-consuming and labor-intensive in clinical practice. Recently, with advancements in computer vision technology, scholars have conducted in-depth research, proposing various fast and accurate tooth segmentation methods. In this review, we review 55 articles in this field and discuss the effectiveness, advantages, and disadvantages of each approach. In addition to simple classification and discussion, this review aims to reveal how tooth segmentation methods can be improved by the application and refinement of existing image segmentation algorithms to solve problems such as irregular morphology and fuzzy boundaries of teeth. It is assumed that with the optimization of these methods, manual operation will be reduced, and greater accuracy and robustness in tooth segmentation will be achieved. Finally, we highlight the challenges that still exist in this field and provide prospects for future directions.

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“…CBCT scans were introduced to dental practices in the United States between 2001 and 2004 [26]. First digital tooth segmentation methods date from 2005 [89] and include techniques such as morphological operators [96], marker-based watershed algorithms [96,94], region growing [90], template-based methods [92,93], graph-cut-based approaches [95] and random forests [91].…”

Section: Cone Beam Computed Tomography (Cbct)mentioning

confidence: 99%

Convolutional Neural Networks in Orthodontics: a review

Płotka¹,

Wlodarczyk²,

Szczerba³

et al. 2021

Preprint

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Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis. This review presents the application of convolutional neural networks in one of the fields of dentistry -orthodontics. Advances in medical imaging technologies and methods allow CNNs to be used in orthodontics to shorten the planning time of orthodontic treatment, including an automatic search of landmarks on cephalometric X-ray images, tooth segmentation on Cone-Beam Computed Tomography (CBCT) images or digital models, and classification of defects on X-Ray panoramic images. In this work, we describe the current methods, the architectures of deep convolutional neural networks used, and their implementations, together with a comparison of the results achieved by them. The promising results and visualizations of the described studies show that the use of methods based on convolutional neural networks allows for the improvement of computer-based orthodontic treatment planning, both by reducing the examination time and, in many cases, by performing the analysis much more accurately than a manual orthodontist does. This note has no numbers. In this work we demonstrate the formation Y_1 of a new type of polariton on the interface between a cuprous oxide slab and a polystyrene micro-sphere placed on the slab.

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3D exemplar‐based random walks for tooth segmentation from cone‐beam computed tomography images

Abstract: The proposed technique enables an efficient and reliable tooth segmentation from CBCT images. This study makes it clinically practical to segment teeth from CBCT images, thus facilitating pre- and interoperative uses of dental morphologies in maxillofacial and orthodontic treatments.

Cited by 18 publications

References 48 publications

Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation

Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation

Semi or fully automatic tooth segmentation in CBCT images: a review

Convolutional Neural Networks in Orthodontics: a review

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