Deep learning in cone-beam computed tomography image segmentation for the diagnosis and treatment of acute pulpitis

Zhang, Xiaoyan; Zhu, Xiaoyan; Xie, Zhiqiang

doi:10.1007/s11227-021-04048-0

J Supercomput

2021

DOI: 10.1007/s11227-021-04048-0

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Deep learning in cone-beam computed tomography image segmentation for the diagnosis and treatment of acute pulpitis

Xiaoyan Zhang

Xiaoyan Zhu

Zhiqiang Xie

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Cited by 3 publications

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“…Rapid, accurate, and robust segmentation of human teeth on CBCT is an important foundation of clinical practice in dentistry. It allows clear visualization of teeth on the one hand, and, is helpful for qualitative evaluation and quantitative analysis of dental diseases such as caries 3,4 , impacted tooth 5 , acute pulpitis 6 , apical periodontitis 7 , root fracture and periodontal lesion 4 . Manual segmentation by experts is usually considered as gold standard.…”

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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

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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.

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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

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Entropy-optimal image segmentation model based on an improved arithmetic optimization algorithm

Xu,

Zhu

2023

J Opt

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The Application of Deep Learning on CBCT in Dentistry

Fan,

Zhang,

Wang

et al. 2023

Diagnostics

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Cone beam computed tomography (CBCT) has become an essential tool in modern dentistry, allowing dentists to analyze the relationship between teeth and the surrounding tissues. However, traditional manual analysis can be time-consuming and its accuracy depends on the user’s proficiency. To address these limitations, deep learning (DL) systems have been integrated into CBCT analysis to improve accuracy and efficiency. Numerous DL models have been developed for tasks such as automatic diagnosis, segmentation, classification of teeth, inferior alveolar nerve, bone, airway, and preoperative planning. All research articles summarized were from Pubmed, IEEE, Google Scholar, and Web of Science up to December 2022. Many studies have demonstrated that the application of deep learning technology in CBCT examination in dentistry has achieved significant progress, and its accuracy in radiology image analysis has reached the level of clinicians. However, in some fields, its accuracy still needs to be improved. Furthermore, ethical issues and CBCT device differences may prohibit its extensive use. DL models have the potential to be used clinically as medical decision-making aids. The combination of DL and CBCT can highly reduce the workload of image reading. This review provides an up-to-date overview of the current applications of DL on CBCT images in dentistry, highlighting its potential and suggesting directions for future research.

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Deep learning in cone-beam computed tomography image segmentation for the diagnosis and treatment of acute pulpitis

Cited by 3 publications

References 18 publications

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

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

Entropy-optimal image segmentation model based on an improved arithmetic optimization algorithm

The Application of Deep Learning on CBCT in Dentistry

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