Izvestiya of Saratov University.

Mathematics. Mechanics. Informatics

ISSN 1816-9791 (Print)
ISSN 2541-9005 (Online)


For citation:

Beskrovny A. S., Bessonov L. V., Ivanov D. V., Zolotov V. S., Sidorenko D. A., Kirillova I. V., Kossovich L. Y. Construction of 3D solid vertebral models using convolutional neural networks. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, 2021, vol. 21, iss. 3, pp. 368-378. DOI: 10.18500/1816-9791-2021-21-3-368-378, EDN: XOVJRW

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
31.08.2021
Full text:
(downloads: 1361)
Language: 
Russian
Heading: 
Article type: 
Article
UDC: 
519.688
EDN: 
XOVJRW

Construction of 3D solid vertebral models using convolutional neural networks

Autors: 
Beskrovny Alexander S., Saratov State University
Bessonov Leonid Valentinovich, Saratov State University
Ivanov Dmitry V., Saratov State University
Zolotov Vladislav S., Saratov State University
Sidorenko Dmitry A., Saratov State University
Kirillova Irina V., Saratov State University
Kossovich Leonid Yurevich, Saratov State University
Abstract: 

The quality of solving the problem of biomechanical modeling largely depends on the created solid-state model of the biological object under study. Building a model based on computed tomography data for a particular patient is possible both in manual mode (software packages for processing medical images) and using automated tools for building a model (image segmentation), which significantly speeds up the process of creating a solid model, in contrast to the manual mode. The complexity of the automated approach lies in the reconstruction of a segmented image into a solid model suitable for biomechanical modeling. As a rule, automatic segmentation is hampered by the presence of anatomical pathologies, noise, and the presence of implants in the images of a digital study. The article proposes a method for creating a solid model from a point cloud obtained from computed tomography data using convolutional neural networks SpatialConfiguration-Net and U-Net. The results of the implementation were applied in the development of the "Module of Solid Models'', which is included in the prototype of the medical decision support system SmartPlan Ortho 3D, which is being developed at Saratov State University within the framework of the project of the Foundation for Advanced Research. The system is included in the register of Russian software.

Acknowledgments: 
The work was supported by the Russian Foundation for Advanced Research (agreement No. 6/130/2018-2021 from 01.08.2018).
References: 
  1. Roth H. R., Le Lu, Lay N., Harrison A. P., Farag A., Sohn A., Summers R. M. Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation. Medical Image Analysis, 2017, vol. 45, pp. 94–107. https://doi.org/10.1016/j.media.2018.01.006
  2. Roth H. R., Hirohisa Oda, Xiangrong Zhou, Natsuki Shimizu, YingYang, Yuichiro Hayashi, Masahiro Oda, Michitaka Fujiwara, Kazunari Misawa, Kensaku Mori. An application of cascaded 3D fully convolutional networks for medical image segmentation. Computerized Medical Imaging and Graphics, 2018, vol. 66, pp. 90–99. https://doi.org/10.1016/j.compmedimag.2018.03.001
  3. Hongya Lu, Haifeng Wang, Qianqian Zhang, Sang Won Yoon, Daehan Won. A 3D convolutional neural network for volumetric image semantic segmentation. Procedia Manufacturing, 2019, vol. 39, pp. 422–428. https://doi.org/10.1016/j.promfg.2020.01.386
  4. Payer C., Stern D., Bischof H., Urschler M. Coarse to fine vertebrae localization and segmentation with SpatialConfiguration-Net and U-Net. In: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, vol. 5: VISAPP. Malta, Valletta, 2020, pp. 124–133. https://doi.org/10.5220/0008975201240133
  5. Kingma D., Ba J. Adam: A method for stochastic optimization. arXiv:1412.6980v9 [cs.LG] 30 Jan 2017. 15 p.
  6. Nesterov Yu. E. A method for solving the convex programming problem with convergence rate O(1/k2 ). Doklady Akademii Nauk SSSR, 1983, vol. 269, no. 3, pp. 543–547 (in Russian).
  7. Lorensen W. E., Cline H. E. Marching cubes: A high resolution 3D surface construction algorithm. ACM SIGGRAPH Computer Graphics, 1987, vol. 21, iss. 4, pp. 163–169. https://doi.org/10.1145/37401.37422
  8. Bugrov N. V., Golubev V. I., Dizhevsky A.Yu., Kakauridze D. G., Klimenko A. S., Oboymov A. S., Frolov P. V. Review of algorithms for triangulating an implicitly defined surface. In: Proceedings of the International Scientific Conference MEDIAS2012. Limasol, Republic of Cyprus, 2012, pp. 151–173 (in Russian).
  9. Skvortsov A. V. Trianguliatsiia Delone i ee primenenie [Delaunay Triangulation and Its Application]. Tomsk, Izd-vo TGU, 2002. 128 p. (in Russian).
  10. Hansen G. A., Douglass R. W., Zardecki A. Mesh Enhancement. (Default Book Series). Imperial College Press, 2005. 532 p. https://doi.org/10.1142/p351
  11. Li Yao, Shihui Huang, Hui Xu, Peilin Li. Quadratic error metric mesh simplification algorithm based on discrete curvature. Mathematical Problems in Engineering, 2015, vol. 2015, article ID 428917. https://doi.org/10.1155/2015/428917
  12. Beskrovny A. S., Bessonov L. V., Ivanov D. V., Kirillova I. V., Kossovich L. Yu. Using a convolutional neural network to automate the construction of two-dimensional solid-state models of vertebrae. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, 2020, vol. 20, iss. 4, pp. 502–516 (in Russian). https://doi.org/10.18500/1816-9791- 2020-20-4-502-516
  13. Ivanov D. V., Kirillova I. V., Kossovich L. Yu., Bessonov L. V., Petraikin A. V., Dol A. V., Ahmad E. S., Morozov S. P., Vladzymyrskyy A. V., Sergunova K. A., Kharlamov A. V. Influence of convolution kernel and beam-hardening effect on the assessment of trabecular bone mineral density using quantitative computed tomography. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, 2020, vol. 20, iss. 2, pp. 205–219. https://doi.org/10.18500/1816-9791-2020-20-2-205-219
Received: 
15.03.2021
Accepted: 
29.04.2021
Published: 
31.08.2021