A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study

Shaheen, Eman; Leite, André Ferreira; Alqahtani, Khalid; Smolders, A.; Gerven, Adriaan Van; Willems, Holger; Jacobs, Reinhilde

doi:10.1016/j.jdent.2021.103865

Cited by 73 publications

(47 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…Moreover, the IOS models can give much more accurate and comprehensive estimates for FDI tooth codes. While Cui's work only focuses on tooth segmentation, we segment teeth and alveolar bones simultaneously 7 . In addition, TSTNet obtains superior segmentation performance with novel auxiliary segmentation heads, loss functions, and augmentations, leading to more clinically applicable 3D models from CBCT.…”

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%

See 2 more Smart Citations

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

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.

show abstract

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

show abstract

“…For instance, CNNs have been applied to automatically detect and segment teeth ( Kuwada et al, 2020 ; Leite et al, 2021 ; Vranckx et al, 2020 ) and cystic lesions ( Kwon et al, 2020 ) in panoramic radiographs. Moreover, examples of applications of CNNs in Cone-Beam Computed Tomography (CBCT) include, mandibular canal segmentation ( Lahoud et al, 2022 ) and tooth segmentation and classification ( Shaheen et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network for automated classification of osteonecrosis and related mandibular trabecular patterns

Saadi

Moreno‐Rabie²,

Wyngaert³

et al. 2022

Bone Reports

Self Cite

View full text Add to dashboard Cite

“…Several U-Nets including 2D U-Net 20,21 , 2.5D U-Net 22 , and 3D U-Net 23 have been proposed for CBCT segmentation. A variant of 2.5D U-Net using majority voting of 2D U-Nets trained by 3 orthogonal imaging planes has been shown to outperform any single U-Net for maxillary and mandibular bony structure segmentation on CBCT 24 .…”

mentioning

confidence: 99%

Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography

Hsu

Yuh

Lin

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Deep learning allows automatic segmentation of teeth on cone beam computed tomography (CBCT). However, the segmentation performance of deep learning varies among different training strategies. Our aim was to propose a 3.5D U-Net to improve the performance of the U-Net in segmenting teeth on CBCT. This study retrospectively enrolled 24 patients who received CBCT. Five U-Nets, including 2Da U-Net, 2Dc U-Net, 2Ds U-Net, 2.5Da U-Net, 3D U-Net, were trained to segment the teeth. Four additional U-Nets, including 2.5Dv U-Net, 3.5Dv5 U-Net, 3.5Dv4 U-Net, and 3.5Dv3 U-Net, were obtained using majority voting. Mathematical morphology operations including erosion and dilation (E&D) were applied to remove diminutive noise speckles. Segmentation performance was evaluated by fourfold cross validation using Dice similarity coefficient (DSC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV). Kruskal–Wallis test with post hoc analysis using Bonferroni correction was used for group comparison. P < 0.05 was considered statistically significant. Performance of U-Nets significantly varies among different training strategies for teeth segmentation on CBCT (P < 0.05). The 3.5Dv5 U-Net and 2.5Dv U-Net showed DSC and PPV significantly higher than any of five originally trained U-Nets (all P < 0.05). E&D significantly improved the DSC, accuracy, specificity, and PPV (all P < 0.005). The 3.5Dv5 U-Net achieved highest DSC and accuracy among all U-Nets. The segmentation performance of the U-Net can be improved by majority voting and E&D. Overall speaking, the 3.5Dv5 U-Net achieved the best segmentation performance among all U-Nets.

show abstract

A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study

Cited by 73 publications

References 18 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

Convolutional neural network for automated classification of osteonecrosis and related mandibular trabecular patterns

Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography

Contact Info

Product

Resources

About