A Symmetric Fully Convolutional Residual Network With DCRF for Accurate Tooth Segmentation

Rao, Yunbo; Wang, Yilin; Meng, Fanman; Pu, Juan; Sun, Jihong; Wang, Qifei

doi:10.1109/access.2020.2994592

Cited by 22 publications

(19 citation statements)

References 41 publications

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“…Most 3D images of teeth originate from CBCT data, and semantic segmentation of these 3D images requires a 3D semantic segmentation network, such as VNet ( 75 ), multi-task 3DFCN and marker-controlled Watershed transform (MWT) ( 76 ), modified UNet ( 77 ), and the symmetric fully convolutional residual network with DCRF ( 78 ). Ezhov et al ( 79 ) proposed a coarse-fine network structure to refine the volumetric segmentation of teeth, with an IoU of 0.94.…”

Section: Clinical Application Of Automatic Image Segmentation In Stomatologymentioning

confidence: 99%

Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application

Luo¹,

Zeng²,

Chen³

et al. 2021

Front. Med. Technol.

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Deep learning has become an active research topic in the field of medical image analysis. In particular, for the automatic segmentation of stomatological images, great advances have been made in segmentation performance. In this paper, we systematically reviewed the recent literature on segmentation methods for stomatological images based on deep learning, and their clinical applications. We categorized them into different tasks and analyze their advantages and disadvantages. The main categories that we explored were the data sources, backbone network, and task formulation. We categorized data sources into panoramic radiography, dental X-rays, cone-beam computed tomography, multi-slice spiral computed tomography, and methods based on intraoral scan images. For the backbone network, we distinguished methods based on convolutional neural networks from those based on transformers. We divided task formulations into semantic segmentation tasks and instance segmentation tasks. Toward the end of the paper, we discussed the challenges and provide several directions for further research on the automatic segmentation of stomatological images.

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Section: Clinical Application Of Automatic Image Segmentation In Stomatologymentioning

confidence: 99%

Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application

Luo¹,

Zeng²,

Chen³

et al. 2021

Front. Med. Technol.

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“…87,88 The U-Net 176 architecture and 2D images obtained from computer tomography (CT) scans were also used, reaching a dice similarity coefficient of 91.7%. 89 The segmentation of oral diseases can increase the performance of the diagnostic process, as the algorithm can then focus on the identified regions of interest. Therefore, several works have segmented the disease prior to the diagnosis for a range of diseases and a range of image types.…”

Section: Dental Image Segmentation and Applicationsmentioning

confidence: 99%

“…81 The use of 2D images has also been explored in literature for tooth detection and segmentation, mainly by employing radiographs, [85][86][87][88] but also 2D images obtained from CBCT scans. 89 Periapical radiographs were used for tooth segmentation using a VGG-16 architecture, reaching a high precision and recall (95.8% and 96.1%, respectively). 85 Similarly, the Resnet-101 architecture was utilized for the same task, obtaining a precision in the tooth detection of 99.6%.…”

Section: Dental Image Segmentation and Applicationsmentioning

confidence: 99%

Applications of artificial intelligence in dentistry: A comprehensive review

Carrillo-Pérez

Pecho

Morales

et al. 2021

J Esthet Restor Dent

120

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Objective To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research. Materials and methods The comprehensive review was conducted in MEDLINE/PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years. Results Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling. Conclusions The insight provided by the present work has reported outstanding results in the design of high‐performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations. Clinical significance The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.

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“…Recently, deep learning methods have been applied in 3D tooth segmentation. Lee et al [4] and Rao et al [5] used…”

Section: Introductionmentioning

confidence: 99%

A Fully Automated Method for 3D Individual Tooth Identification and Segmentation in Dental CBCT

Jang

Kim

Cho

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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Accurate and automatic segmentation of three-dimensional (3D) individual teeth from cone-beam computerized tomography (CBCT) images is a challenging problem because of the difficulty in separating an individual tooth from adjacent teeth and its surrounding alveolar bone. Thus, this paper proposes a fully automated method of identifying and segmenting 3D individual teeth from dental CBCT images. The proposed method addresses the aforementioned difficulty by developing a deep learning-based hierarchical multi-step model. First, it automatically generates upper and lower jaws panoramic images to overcome the computational complexity caused by high-dimensional data and the curse of dimensionality associated with limited training dataset. The obtained 2D panoramic images are then used to identify 2D individual teeth and capture loose-and tight-regions of interest (ROIs) of 3D individual teeth. Finally, accurate 3D individual tooth segmentation is achieved using both loose and tight ROIs. Experimental results showed that the proposed method achieved an F1-score of 93.35% for tooth identification and a Dice similarity coefficient of 94.79% for individual 3D tooth segmentation. The results demonstrate that the proposed method provides an effective clinical and practical framework for digital dentistry.

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A Symmetric Fully Convolutional Residual Network With DCRF for Accurate Tooth Segmentation

Cited by 22 publications

References 41 publications

Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application

Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application

Applications of artificial intelligence in dentistry: A comprehensive review

A Fully Automated Method for 3D Individual Tooth Identification and Segmentation in Dental CBCT

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