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

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Title: A Fully Automated Method for 3D Individual Tooth Identification and Segmentation in Dental CBCT.
Authors: Jang, Tae Jun1 taejunjang@yonsei.ac.kr, Kim, Kang Cheol1 kangcheol@yonsei.ac.kr, Cho, Hyun Cheol1 whguscjf55@yonsei.ac.kr, Seo, Jin Keun1 seoj@yonsei.ac.kr
Source: IEEE Transactions on Pattern Analysis & Machine Intelligence. Oct2022, Vol. 44 Issue 10, p6562-6568. 7p.
Subjects: Cone beam computed tomography, Deep learning, Computed tomography, Teeth, Maxilla, Mandible
Abstract: 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 percent for tooth identification and a Dice similarity coefficient of 94.79 percent for individual 3D tooth segmentation. The results demonstrate that the proposed method provides an effective clinical and practical framework for digital dentistry. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Pattern Analysis & Machine Intelligence is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Fully Automated Method for 3D Individual Tooth Identification and Segmentation in Dental CBCT.
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  Data: <searchLink fieldCode="AR" term="%22Jang%2C+Tae+Jun%22">Jang, Tae Jun</searchLink><relatesTo>1</relatesTo><i> taejunjang@yonsei.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Kim%2C+Kang+Cheol%22">Kim, Kang Cheol</searchLink><relatesTo>1</relatesTo><i> kangcheol@yonsei.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Cho%2C+Hyun+Cheol%22">Cho, Hyun Cheol</searchLink><relatesTo>1</relatesTo><i> whguscjf55@yonsei.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Seo%2C+Jin+Keun%22">Seo, Jin Keun</searchLink><relatesTo>1</relatesTo><i> seoj@yonsei.ac.kr</i>
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  Data: <searchLink fieldCode="DE" term="%22Cone+beam+computed+tomography%22">Cone beam computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Teeth%22">Teeth</searchLink><br /><searchLink fieldCode="DE" term="%22Maxilla%22">Maxilla</searchLink><br /><searchLink fieldCode="DE" term="%22Mandible%22">Mandible</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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 percent for tooth identification and a Dice similarity coefficient of 94.79 percent for individual 3D tooth segmentation. The results demonstrate that the proposed method provides an effective clinical and practical framework for digital dentistry. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Pattern Analysis & Machine Intelligence is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1109/TPAMI.2021.3086072
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      – Code: eng
        Text: English
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      – SubjectFull: Cone beam computed tomography
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Computed tomography
        Type: general
      – SubjectFull: Teeth
        Type: general
      – SubjectFull: Maxilla
        Type: general
      – SubjectFull: Mandible
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      – TitleFull: A Fully Automated Method for 3D Individual Tooth Identification and Segmentation in Dental CBCT.
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            NameFull: Jang, Tae Jun
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            NameFull: Kim, Kang Cheol
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              M: 10
              Text: Oct2022
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