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., Kirillova I. V., Kossovich L. Y. Using the Mask-RCNN Convolutional Neural Network to Automate the Construction of Two-Dimensional Solid Vertebral Models. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, 2020, vol. 20, iss. 4, pp. 502-516. DOI: 10.18500/1816-9791-2020-20-4-502-516, EDN: JWTDAH

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
30.11.2020
Full text:
(downloads: 304)
Language: 
Russian
Heading: 
Article type: 
Article
UDC: 
501.1
EDN: 
JWTDAH

Using the Mask-RCNN Convolutional Neural Network to Automate the Construction of Two-Dimensional Solid Vertebral Models

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

Biomechanical modeling requires the construction of an accurate solid model of the object under study based on the data of a particular patient. This problem can be solved manually using modern software packages for medical data processing or using computer-aided design systems. This approach is used by many researchers and allows you to create accurate solid models, but is time consuming. In this regard, the automation of the construction of solid models suitable for performing biomechanical calculations is an urgent task and can be carried out using neural network technologies. This study presents the implementation of one of the methods for processing computed tomography data in order to create two-dimensional accurate solid models of vertebral bodies in a sagittal projection. An artificial neural network Mask-RCNN was used for automatic recognition of vertebrae. The assessment of the quality of the automatic recognition performed by the neural network was carried out on the basis of comparison with the S¨ orensen measure with manual segmentation performed by practitioners. Application of the method makes it possible to significantly speed up the process of modeling bone structures of the spine in 2D mode. The implemented technique was used in the development of a solid-state model module, which is included in the SmartPlan Ortho 2D medical decision support system developed at Saratov State University within the framework of the Advanced Research Foundation project.

References: 
  1. Kazhanov I. V., Mikityuk S. I., Dol’ А.V., Ivanov D. V., Kharlamov А. V., Petrov А. V., Kossovich L. Yu., Manukovskiy V. A. Biomechanical Modeling of Options for Internal Fixation of Unilateral Fractures of the Sacrum. Traumatology and Orthopedics of Russia, 2020, vol. 26, no. 2, pp. 79–90 (in Russian). DOI: https://doi.org/10.21823/2311-2905-2020-26-2-79-90
  2. Kudjashev A. L., Hominec V. V., Teremshonok A. V., Korostelev K. E., Nagornyj E. B., Dol A. V., Ivanov D. V., Kirillova I. V., Kossovich L. Yu. Biomechanical background for the formation of proximal junctional kyphosis after the transpedicular fixation of the lumbarian spine. Russian Journal of Biomechanics, 2017, vol. 21, no. 3, pp. 270–278.
  3. Donnik A. M., Ivanov D. V., Kossovich L. Ju., Levchenko K. K., Kireev S. I., Morozov K. M., Ostrovskij N. V., Zareckov V. V., Lihachev S. V. Creation of 3D Solid Models of the Spine with Transpedicular Fixation Using Specialized Software. Izv. Saratov Univ. (N. S.), Ser. Math. Mech. Inform., 2019, vol. 19, iss. 4, pp. 424–438 (in Russian). DOI: https://doi.org10.18500/1816-9791-2019-19-4-424-438
  4. Stewart R. D., Fermin I., Opper M.Region growing with pulse-coupled neural networks: An alternative to seeded region growing. IEEE Trans. on Neural Networks, 2002, vol. 13, iss. 6, pp. 1557–1562. DOI: https://doi.org10.1109/TNN.2002.804229
  5. Chandhok C. A Novel Approach to Image Segmentation using Artificial Neural Networks and K-Means Clustering. International Journal of Engineering Research and Applications, 2012, vol. 2, iss. 3, pp. 274–279. DOI: https://doi.org10.1.1.416.9795
  6. Belim S. V., Larionov S. B. Image segmentation algorithm using an artificial neural network without using other images. Radio Engineering, 2017, no. 3, pp. 43–53 (in Russian). DOI: https://doi.org10.24108/rdopt.0317.0000108
  7. Dol D., Dol A., Bessonov L., Ivanov. D., Beskrovny A., Falkovich A., Ostrovsky N. Methods of constructing an outline simple closed contour for modeling functional spine unit on CT slice. Progress in Biomedical Optics and Imaging — Proceedings of SPIE, 2020, vol. 11229, 112291Q. DOI: https://doi.org/10.1117/12.2545013
  8. Medvedev D. G. Algorithm for highlighting the outline of an object with fuzzy edges. Aktual’nye problemy gumanitarnykh i estestvennykh nauk [Actual problems of the humanities and natural sciences], 2014, vol. 2, no. 1, pp. 56–61 (in Russian).
  9. Withey D. J., Koles Z. J. Medical Image Segmentation: Methods and Software. Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging. Hangzhou, 2007, pp. 140–143. DOI: https://doi.org/10.1109/NFSI-ICFBI.2007.4387709
  10. Beskrovny A. S., Makhankov A. V., Bessonov L. V., Lemeshkin M. O. Application of artificial neural network technologies to vertebral segmentation according on CT data. Progress in Biomedical Optics and Imaging — Proceedings of SPIE, 2020, vol. 11229, 112291Y. DOI: https://doi.org/10.1117/12.2545001
  11. Kolesnikova A. S., Bessonov L. V., Luneva A D., Dmitriev P. O., Matershev I. V., Kurchatkin A. A., Zolotov V. S., Sidorenko D. A., Chuvashkin V. K., Varjuhin A. A., Gushhina S. G. Development of the approach for improvements method of active circles. Information Technologies for the Physician, 2018, vol. 3, pp. 61–72 (in Russian).
  12. Long J., Shelhamer E., Darrell T. Fully convolutional networks for semantic segmentation. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, Massachusetts, 2015, pp. 3431–3440. DOI: https://doi.org/10.1109/CVPR.2015.7298965
  13. Badrinarayanan V., Kendall A., Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 2017, vol. 39, no. 12, pp. 2481–2495. DOI: https://doi.org/10.1109/TPAMI.2016.2644615
  14. Kamnitsas K., Ferrante E., Parisot S., Ledig C., Nori A. V., Criminisi A., Rueckert D., Glocker B. DeepMedic for Brain Tumor Segmentation. In: Crimi A., Menze B., Maier O., Reyes M., Winzeck S., Handels H. (eds.). Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes, 2016. Lecture Notes in Computer Science, vol. 10154. Springer, Cham, pp. 138–149. DOI: https://doi.org/10.1007/978-3-319-55524-9_14
  15. Noh H., Hong S., Han B. Learning deconvolution network for semantic segmentation. 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, 2015, pp. 1520– 1528. DOI: https://doi.org/10.1109/ICCV.2015.178
  16. Chaurasia A., Culurciello E. LinkNet: Exploiting encoder representations for efficient semantic segmentation. 2017 IEEE Visual Communications and Image Processing (VCIP). St. Petersburg, FL, 2017, pp. 1–4. DOI: https://doi.org/10.1109/vcip.2017.8305148
  17. He K., Gkioxari G., Doll´ar P., Girshick R. Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV). Venice, 2017, pp. 2980–2988. DOI: https://doi.org10.1109/ICCV.2017.322
  18. He K., Zhang X., Ren S., Sun J. Deep Residual Learning for Image Recognition 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, 2016, pp. 770–778. DOI: https://doi.org10.1109/CVPR.2016.90
  19. Samarskij A. A., Gulin A. V. Chislennye metody [Numerical methods]. Moscow, Nauka, 1989. 432 p. (in Russian).
  20. Yao A. D., Cheng D. L., Pan I., Kitamura F. Deep Learning in Neuroradiology: A Systematic Review of Current Algorithms and Approaches for the New Wave of Imaging Technology. Radiology: Artificial Intelligence, 2020, vol. 2, no. 2, pp. 6. DOI: https://doi.org10.1148/ryai.2020190026
  21. 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. Izv. Saratov Univ. (N. S.), Ser. Math. Mech. Inform., 2020. vol. 20, iss. 2, pp. 205–219. DOI: https://doi.org10.18500/1816-9791-2020-20-2-205-219
  22. Sekuboyina A., Bayat A., Husseini M. E., L¨offler M., Rempfler M., Kukacka J., Tetteh G., Valentinitsch A., Payer C., Urschler M., Chen M., Cheng D., Lessmann N., Hu Y., Wang T., Yang D., Xu D., Ambellan F., Zachow S., Jiang T., Ma X., Angerman Ch., Wang X., Wei Q. Brown K., Wolf M., Kirszenberg A., Puybareauq ´ E., Menze B. H., Kirschke J. VerSe: A Vertebrae Labelling and Segmentation Benchmark. Computer Science, Engineering, ArXiv, arXiv:2001.09193[cs.CV], 2020, 30 p.
Received: 
19.05.2019
Accepted: 
30.06.2019
Published: 
30.11.2020