skip to main content
10.1145/3638985.3638995acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicitConference Proceedingsconference-collections
research-article

Accurate segmentation based on heuristic shape attention for occlusal tooth on CBCT

Published: 11 March 2024 Publication History

Abstract

Accurate occlusal tooth segmentation for dental CBCT is essential for traumatic occlusal force diagnosis. As tiny multi-objects, adhesion from surrounding touching would largely hinder the correct detection. Especially, the convex and concave occlusal surface appears irregular and unconnected and increase the difficulties for separation. We proposed a three-stage segmentation framework. First, the panoramic view is incorporated for better dental spatial relationship understanding. Second, a tracking based on heuristic shape is specially designed for the adhesions among occlusal crown. Finally, the heuristic shape is further encoded as an attention in an improved 3DUnet to enhance the segmentation accuracy. To get the specific features of our dataset without large-scale samples, we propose a strategy from 2D coarse to 3D fine segmentation to reduce the unnecessary computation. In experiments, comparison and ablation studies demonstrate that our method generates more accurate results and outperforms other three state-of-the-art segmentations.

References

[1]
Ruben Pauwels, Reinhilde Jacobs, Hilde Bosmans, and Pisha Pittayapat.2014. Automated implant segmentation in cone-beam ct using edge detection and particle counting. International journal of computer assisted radiology and surgery 9, 4, 733–743. https://doi.org/10.1007/s11548-013-0946-z
[2]
Ionel-Bujorel Pavaloiu, Nicolae Goga, Andrei Vasilateanu, and Iuliana Marin. 2015. Neural network-based edge detection for cbct segmentation. 2015 E-Health and Bioengineering Conference (EHB), IEEE, 2015,1-4. https://doi.org/10.1109/EHB.2015.7391414
[3]
Jérôme Michetti, Adrian Basarab, Franck Diemer, and Denis Kouame. 2017. Comparison of an adaptive local thresholding method on cbct and μct endodontic images. Physics in Medicine & Biology 63, 1, 015020. https:// doi.org/10.1088/1361-6560/aa90ff
[4]
Kanungo T, Mount D M, and Netanyahu N S. 2002. An efficient k-means clustering algorithm: analysis and implementation. IEEE Computer Society 2002,7. https:// doi.org/10.1109/TPAMI.2002.1017616.
[5]
Rumelhart D E, Hinton G E, and Williams R J. 1986. Learning Representations by Back Propagating Errors. Nature 323, 6088, 533-536. https:// doi.org/10.1038/323533a0.
[6]
Hosntalab M, Zoroofi R A, and Shirani T F. Segmentation of teeth in CT volumetric dataset by panoramic projection and variational level set. International Journal of Computer Assisted Radiology&Surgery 2008. https:// doi.org/10.1007/s11548-008-0230-9.
[7]
Wang L, Gao Y, and Shi F. 2016. Automated segmentation of dental CBCT image with prior-guided sequential random forests. Medical Physics 43,1, 336-346. https:// doi.org/10.1118/1.4938267.
[8]
Arifin A Z, Tanuwijaya E, and Nugroho B. 2019. Automatic Image Slice Marking Propagation on Segmentation of Dental CBCT. TELKOMNIKA Indonesian Journal of Electrical Engineering 17, 6, 3218-3225. https:// doi.org/10.12928/TELKOMNIKA.v17i6.13220.
[9]
Verhelst P, Smolders A, and Beznik T. 2021. Layered deep learning for automatic mandibular segmentation in cone-beam computed tomography. Journal of dentistry 114,103786. https:// doi.org/10.1016/j.jdent.2021.103786.
[10]
Bingjiang Qiu, Hylke van der Wel, Joep Kraeima, and Haye Hendrik Glas. 2021. Accurate Mandible Segmentation on Dental CBCT Scans Affected by Metal Artifacts Using a Prior Shape Model. Journal of Personalized Medicine, 2021, 11. https:// doi.org/10.3390/jpm11050364.
[11]
Gloria Hyunjung Kwak, Eun-Jung Kwak, Jae Min Song, and Hae Ryoun Park. 2020. Automatic mandibular canal detection using a deep convolutional neural network. Scientific Reports 10, 5711. https://doi.org/10.1038/s41598-020-62586-8
[12]
Cui, Zhiming, Changjian Li, and Wenping Wang. 2019. Automatic Tooth Instance Segmentation and Identification from Cone Beam CT Images. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 2019, 6368-6377. https:// doi.org/10.1109/CVPR.2019.00653.
[13]
Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. 2021. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021, 7262-7272. http://arxiv.org/abs/2105.05633.
[14]
Zeyu Chen, Senyang Chen, and Fengjun Hu. 2023. CTA-UNet: CNN-transformer architecture UNet for dental CBCT images segmentation Phys. Med. Biol. 68, 175042. https:// doi.org/10.1088/1361-6560/acf026
[15]
Shen Gao, Xuguang Li, Xin Li, and Zhen Li. 2022. Transformer based tooth classification from cone-beam computed tomography for dental charting. Computers in Biology and Medicine 2022, 148, 105880. https://doi.org/10.1016/j.compbiomed.2022.105880
[16]
Shangxuan Li, Chichi Li, Yu Du, Li Ye, and Yanshu Fang. 2023. Transformer-Based Tooth Segmentation, Identification and Pulp Calcification Recognition in CBCT. International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland 2023, 706-714. https://doi.org/10.1007/978-3-031-43904-9_68.
[17]
Liu Ze, Lin Yutong, Cao Yue, and Wei, Yixuan. 2021. Swin transformer: Hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF international conference on computer vision 2021,10012-10022. https:// doi.org/10.48550/arXiv.2103.14030
[18]
Zeyang Xia, Yangzhou Gan, Lichao Chang, and Jing Xiong. 2017. Individual tooth segmentation from CT images scanned with contacts of maxillary and mandible teeth. Computer Methods & Programs in Biomedicine, 138, 1-12. https:// doi.org/10.1016/j.cmpb.2016.10.002.
[19]
J Tae Jun Jang, Kang Cheol Kim, Hyun Cheol Cho, and Jin Keun Seo. 2021. A fully automated method for 3D individual tooth identification and segmentation in dental CBCT. IEEE transactions on pattern analysis and machine intelligence, 44,10, 6562-6568. https:// doi.org/10.1109/TPAMI.2021.3086072.
[20]
Pengcheng Li, Yang Liu, Zhiming Cui, Feng Yang, and Yue Zhao. 2022. Semantic graph attention with explicit anatomical association modeling for tooth segmentation from CBCT images. IEEE Transactions on Medical Imaging, 41, 11, 3116-3127. https:// doi.org/ 10.1109/TMI.2022.3179128.
[21]
K. K. Thanammal, J. S. Jayasudha, R. R. Vijayalakshmi, and S. Arumugaperumal, "Effective Histogram Thresholding Techniques for Natural Images Using Segmentation," Journal of Image and Graphics, Vol. 2, No. 2, pp. 113-116, December 2014.
[22]
Rupinder Kaur and Er. Garima Malik, "An Image Segmentation Using Improved FCM Watershed Algorithm and DBMF," Journal of Image and Graphics, Vol. 2, No. 2, pp. 106-112, December 2014.
[23]
Noël Richard, Christine Fernandez-Maloigne, Cristian Bonanomi, and Alessandro Rizzi, "Fuzzy Color Image Segmentation using Watershed Transform," Journal of Image and Graphics, Vol. 1, No. 3, pp. 157-160, September 2013.

Index Terms

  1. Accurate segmentation based on heuristic shape attention for occlusal tooth on CBCT

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICIT '23: Proceedings of the 2023 11th International Conference on Information Technology: IoT and Smart City
    December 2023
    266 pages
    ISBN:9798400709043
    DOI:10.1145/3638985
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 11 March 2024

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. CBCT images
    2. Occlusion
    3. Segmentation
    4. Semantic Shape Attention

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    • the Natural Science Foundation of Fujian Province
    • The Young and Middle-aged Teachers Foundation of Fujian Province

    Conference

    ICIT 2023
    ICIT 2023: IoT and Smart City
    December 14 - 17, 2023
    Kyoto, Japan

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 11
      Total Downloads
    • Downloads (Last 12 months)11
    • Downloads (Last 6 weeks)2
    Reflects downloads up to 20 Jan 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media