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Computational Modelling and Machine Learning Based Image Processing in Spine Research

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Personalized Orthopedics

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Abstract

Diagnosing spinal problems is not an easy task. Doctors collect different types of information, including magnetic resonance imaging (MRI), to make a final diagnosis and decision on treatment modality. The localization of lumbar discs on MRI images is a challenging problem due to the wide range of variability in size, shape, number and appearance of discs and vertebrae. Current state-of-the art studies show that most of the implemented methods are semi-automatic and suffer from additional correction of the solution or are very sensitive to the changes in parameters. This chapter will use two different approaches—computational modelling using finite element method to investigate the displacements and stress distribution and machine learning (ML) algorithms to perform automatic segmentation of regions of interest (vertebrae, discs). The results for segmentation show high accuracy, with possibilities for improvement. Finite element analysis, performed on a 3-dimensional model automatically created from scans using ML, for a healthy and herniated disc, can provide an additional insight into the processes and different effect of the herniated disc onto the spine (i.e. back pain). A computer diagnostic system can be helpful in generating diagnostic results in short time and represent a help in final decision making.

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Acknowledgements

This research is funded by Serbian Ministry of Education, Science, and Technological Development [451-03-68/2020-14/200107 (Faculty of Engineering, University of Kragujevac)]. This research is also supported by the projects that have received funding from the European Union’s Horizon 2020 research and innovation programmes under grant agreements No 952603 (SGABU project) and No 760921 (PANBioRA project). This article reflects only the author's view. The Commission is not responsible for any use that may be made of the information it contains.

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Authors and Affiliations

  1. Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia

    Tijana Šušteršič, Vojin Kovačević, Vesna Ranković, Lukas Rasulić & Nenad Filipović

  2. Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia

    Tijana Šušteršič, Vojin Kovačević, Vesna Ranković, Lukas Rasulić & Nenad Filipović

  3. Steinbeis Advanced Risk Technologies Institute Doo Kragujevac (SARTIK), Kragujevac, Serbia

    Tijana Šušteršič, Vojin Kovačević, Vesna Ranković, Lukas Rasulić & Nenad Filipović

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  1. Tijana Šušteršič
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Correspondence to Nenad Filipović .

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  1. Polytechnic School, Pontifical Catholic University of Paraná, Curitiba, Paraná, Brazil

    Osiris Canciglieri Junior

  2. Faculty of Mechanical Engineering, University of Nis, Nis, Serbia

    Miroslav D. Trajanovic

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Šušteršič, T., Kovačević, V., Ranković, V., Rasulić, L., Filipović, N. (2022). Computational Modelling and Machine Learning Based Image Processing in Spine Research. In: Canciglieri Junior, O., Trajanovic, M.D. (eds) Personalized Orthopedics. Springer, Cham. https://doi.org/10.1007/978-3-030-98279-9_16

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