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

Jang, Tae Jun; Kim, Kang Cheol; Cho, Hyun Cheol; Seo, Jin Keun

doi:10.1109/tpami.2021.3086072

Cited by 81 publications

(53 citation statements)

References 30 publications

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“…Cui et al only generated tooth codes for cases with 28 teeth, but that could not be applied to other tooth codes, like third molars 21 . Tae et al designed a tooth identification method based on 2D panoramic images reconstructed from CBCT to identify incisors, canines, premolar, and molars, then assign FDI tooth codes accordingly 6 . However, the two-stage classification procedure is error-prone, and it cannot handle complicated cases like missing teeth or hyperdontia, as the tooth numbers are not inherently provided.…”

Section: Discussionmentioning

confidence: 99%

“…By doing so, we substantially reduce human labor for annotation, as well as network complexity and corresponding training efforts for segmentation. For example, hierarchical or multi-stage networks are not needed [6][7]9 . Moreover, the IOS models can give much more accurate and comprehensive estimates for FDI tooth codes.…”

Section: Discussionmentioning

confidence: 99%

“…Recent efforts have been made towards the automatic segmentation of teeth and bones in CBCT images based on deep learning [5][6][7][8][9] . These approaches can generate highly efficient segmentation results of tooth-bone structures for digital model visualization.…”

Section: Introductionmentioning

confidence: 99%

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AI-enabled Automatic Multimodal Fusion of Cone-Beam CT and Intraoral Scans for Intelligent 3D Tooth-Bone Reconstruction and Clinical Applications

Hao

Liu

Lin

et al. 2022

Preprint

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Cone-beam computed tomography (CBCT) is one of the most widely used digital models in dental practices. A critical step in virtual dental treatment planning is to accurately delineate all tooth-bone structures from CBCT with high fidelity. Previous studies have established several methods for CBCT segmentation using deep learning. However, the inherent resolution discrepancy and the loss of occlusal information in CBCT largely limited its clinical applicability. Here, we present a Deep Dental Multimodal Analysis (DDMA) framework consisting of a CBCT segmentation model, an intraoral scan (IOS) segmentation model (the most accurate digital dental model), and a fusion model to generate 3D fused tooth-bone structures with high fidelity and accurate occlusal information. Our model was trained with a large-scale dataset with 503 CBCT and 28,559 IOS meshes manually annotated by human experts. For CBCT segmentation, our framework achieves an average Dice coefficient of 93.99%, significantly outperforming the baselines. For IOS segmentation, our model achieves a mIoU of 93.07% and 95.70% on the maxillary and mandible on a test dataset of 200 IOS meshes, 1.77% and 3.52% higher than the state-of-art method. In addition, our DDMA framework takes 20 to 25 minutes to generate the fused model, compared to over 5 hours by human experts. Notably, real-world clinical cases demonstrate that our model can visualize tooth-bone structures and relationships during the entire orthodontic treatment and predict clinical risks like dehiscence and fenestration. These findings demonstrate the potential of deep multimodal learning to improve the quality of digital dental models and guide clinical treatment and diagnostic decisions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

AI-enabled Automatic Multimodal Fusion of Cone-Beam CT and Intraoral Scans for Intelligent 3D Tooth-Bone Reconstruction and Clinical Applications

Hao

Liu

Lin

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…The limitation of this paper is that practical examination of the proposed directions for 3D CNN implementations will require some time. Currently, there are many different 3D CNN in development, and actually, this is where most of the research activity is carried out [151,[153][154][155].…”

Section: Discussionmentioning

confidence: 99%

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

et al. 2021

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Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are potent in image processing and recognition using deep learning to perform generative and descriptive tasks. Compared to its predecessor, the advantage of CNN is that it automatically detects the important features without any human supervision. 3D CNN is used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures, e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers, emphasizing activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore the current and design customized 3D CNN application methods for particular forensic research in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

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“…The crowns were considered to be made of gold. We adopted an existing segmentation method 44 for individual tooth segmentation.…”

Section: Methodsmentioning

confidence: 99%

A fidelity‐embedded learning for metal artifact reduction in dental CBCT

et al. 2022

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Purpose: Dental cone-beam computed tomography (CBCT) has been increasingly used for dental and maxillofacial imaging. However, the presence of metallic inserts, such as implants, crowns, and dental braces, violates the CT model assumption, which leads to severe metal artifacts in the reconstructed CBCT image, resulting in the degradation of diagnostic performance. In this study, we used deep learning to reduce metal artifacts. Methods: The metal artifacts, appearing as streaks and shadows, are nonlocal and highly associated with various factors, including the geometry of metallic inserts, energy-dependent attenuation, and energy spectrum of the incident X-ray beam, making it difficult to learn their complicated structures directly. To provide a step-by-step environment in which deep learning can be trained, we propose an iterative learning approach in which the network at each iteration step learns the correction error caused by the previous network, while enforcing the data fidelity in the projection domain. To generate a realistic paired training dataset, metal-free CBCT scans were collected from patients without metallic inserts, and then simulated metal projection data were added to generate the corresponding metal-corrupted projection data. Results: The feasibility of the proposed method was investigated in clinical metal-affected CBCT scans, as well as simulated metal-affected CBCT scans. The results show that the proposed method significantly reduces metal artifacts while preserving the morphological structures near metallic objects and outperforms direct image domain learning. Conclusion:The proposed fidelity-embedded learning can effectively reduce metal artifacts in dental CBCT compared with direct image domain learning.

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

Cited by 81 publications

References 30 publications

AI-enabled Automatic Multimodal Fusion of Cone-Beam CT and Intraoral Scans for Intelligent 3D Tooth-Bone Reconstruction and Clinical Applications

AI-enabled Automatic Multimodal Fusion of Cone-Beam CT and Intraoral Scans for Intelligent 3D Tooth-Bone Reconstruction and Clinical Applications

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

A fidelity‐embedded learning for metal artifact reduction in dental CBCT

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