Novosibirsk State University Journal of Information Technologies
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All Issues >> Contents: Volume 15, Issue No 3 (2017)

Restoration of the 3D Skull Defect Model Based on Deep Neural Networks
E. N. Pavlovsky, D. V. Pakulich, S. O. Pospelov

Novosibirsk State University

DOI: DOI 10.25205/1818-7900-2017-15-3-74-78
UDC code: 004.852

Abstract
The article is devoted to the creation of a method for automatic modeling of the 3D skull defect. A method based on a deep neural network is proposed, which allows creating with a reasonable accuracy a 3D model of the lost part of the skull, regardless of the localization of the defect.

Key Words
deep neural networks, 3D, skull, cranioplasty, autoencoder

How to cite:
Pavlovsky E. N., Pakulich D. V., Pospelov S. O. Restoration of the 3D Skull Defect Model Based on Deep Neural Networks // Vestnik NSU Series: Information Technologies. - 2017. - Volume 15, Issue No 3. - P. 74–78. - DOI 10.25205/1818-7900-2017-15-3-74-78. - ISSN 1818-7900. (in Russian).

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References
1. Yuan X., Hu Q., Liu H., Dai C., Fang M. Modeling Technology and Application of Repairing Bone Defects Based on Rapid Prototyping // Knowledge Enterprise: Intelligent Strategies in Product Design, Manufacturing, and Management. IFIP International Federation for Information Processing / Eds. K. Wang, G. L. Kovacs, M. Wozny, M. Fang. Springer, Boston, MA, 2006. Vol. 207.
2. Chulvi V. et al. Knowledge-Based Engineering in cranioplasty implant design // Proc. of the 16th International Conference on Engineering Design (ICED’07). Paris. 2007. Vol. 4.
3. Kim B.-J. et al. Customized Cranioplasty Implants Using Three-Dimensional Printers and Polymethyl-Methacrylate Casting // Journal of Korean Neurosurgical Society. 2012. № 52. P. 541–546.
4. Hsu J.-H., Tseng C.-S. Application of three-dimensional orthogonal neural network to craniomaxillary reconstruction // Comput. Med. Imaging Graph. 2001. № 6. P. 477–482.
5. Zhang Z., Song Z. Skull defect reconstruction based on a new hybrid level set // Biomed. Mater. Eng. 2014. № 6. P. 3343–3351. 6. Shcherbakov O., Batishcheva V. Image inpainting based on stacked autoencoders // Journal of Physics. 2014. doi:10.1088/1742-6596/536/1/012020.

Publication information
Main title Vestnik NSU Series: Information Technologies, Volume 15, Issue No 3 (2017).
Parallel title: Novosibirsk State University Journal of Information Technologies Volume 15, Issue No 3 (2017).

Key title: Vestnik Novosibirskogo gosudarstvennogo universiteta. Seriâ: Informacionnye tehnologii
Abbreviated key title: Vestn. Novosib. Gos. Univ., Ser.: Inf. Tehnol.
Variant title: Vestnik NGU. Seriâ: Informacionnye tehnologii

Year of Publication: 2017
ISSN: 1818-7900 (Print), ISSN 2410-0420 (Online)
Publisher: Novosibirsk State University Press
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