Automatic Mandible Segmentation Using Convolutional Neural Networks and Feature Sharing
Abstract
Mandible segmentation is a crucial step in various dental procedures and is the focus of this work. It uses a new neural network for it. The initial results were promising, achieving 98% accuracy. However, due to class imbalance, these results did not fully reflect reality. The customized architecture is still in the testing phase and has not been fully evaluated, so no conclusive results have been obtained thus far. Nevertheless, it is undergoing extensive validation and demonstrates strong potential to significantly advance automation in dental imaging.
Keywords:
Mandible, Segmentation, Neural, Networks, U-net, Features, Sharing
References
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Bingjiang Qiu, Jiapan Guo, Joep Kraeima, Haye Hendrik Glas, Weichuan Zhang, Ronald JH Borra, Max Johannes Hendrikus Witjes, and Peter MA van Ooijen. 2021. Recurrent convolutional neural networks for 3D mandible segmentation in computed tomography. Journal of personalized medicine (2021).
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Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. Springer, 234–241.
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Aura Conci, Stephenson S.L. Galvão, Giomar O. Sequeiros, Débora C.M. Saade, and Trueman MacHenry. 2015. A new measure for comparing biomedical regions of interest in segmentation of digital images. Discrete Applied Mathematics 197 (2015), 103–113. DOI: 10.1016/j.dam.2015.06.002 Distance Geometry and Applications.
Robert R. Iles, an, Michel Beyer, Christoph Kunz, and Florian M. Thieringer. [n. d.]. Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software. Bioengineering 10, 5 ( [n. d.]). [link]
Talal Bin Irshad, Giulia Pascoletti, Francesco Bianconi, and Elisabetta M Zanetti. 2024. Mandibular bone segmentation from CT scans: Quantitative and qualitative comparison among software. Dental Materials 40, 8 (2024), e11–e22.
Tobias Pankert, Hyun Lee, Florian Peters, Frank Hölzle, Ali Modabber, and Stefan Raith. 2023. Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network. International Journal of Computer Assisted Radiology and Surgery 18, 8 (2023), 1479–1488.
Bingjiang Qiu, Jiapan Guo, Joep Kraeima, Haye Hendrik Glas, Weichuan Zhang, Ronald JH Borra, Max Johannes Hendrikus Witjes, and Peter MA van Ooijen. 2021. Recurrent convolutional neural networks for 3D mandible segmentation in computed tomography. Journal of personalized medicine (2021).
Bingjiang Qiu, Hylke van der Wel, Joep Kraeima, Haye Hendrik Glas, Jiapan Guo, Ronald JH Borra, Max Johannes Hendrikus Witjes, and Peter MA van Ooijen. 2021. Automatic segmentation of mandible from conventional methods to deep learning—a review. Journal of personalized medicine 11, 7 (2021), 629.
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. Springer, 234–241.
Muhammad Usman, Azka Rehman, Amal Muhammad Saleem, Rabeea Jawaid, Shi-Sub Byon, Sung-Hyun Kim, Byoung-Dai Lee, Min-Suk Heo, and Yeong-Gil Shin. 2022. Dual-stage deeply supervised attention-based convolutional neural networks for mandibular canal segmentation in CBCT scans. Sensors 22, 24 (2022), 9877.
Published
2025-06-03
How to Cite
CAVALCANTE, Lucas Mendonça de Morais; CONCI, Aura; FERNANDES, Leandro A. F..
Automatic Mandible Segmentation Using Convolutional Neural Networks and Feature Sharing. In: ACM INTERNATIONAL CONFERENCE ON INTERACTIVE MEDIA EXPERIENCES WORKSHOPS (IMXW), 25. , 2025, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 58-63.
DOI: https://doi.org/10.5753/imxw.2025.7130.
