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
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Language: 
Russian
Heading: 
Computer Sciences
Article type: 
Article
UDC: 
501.1
DOI: 
10.18500/1816-9791-2020-20-4-502-516
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.

Key words: 
SmartPlan Ortho 2D
solid model
biomechanical modeling
spinal pelvic complex
computer tomography
DICOM
convolutional neural network
Mask-RCNN
2D segmentation
Sorensen measure
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Received: 
19.05.2019
Accepted: 
30.06.2019
Published: 
30.11.2020
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