Skip to main content
SpringerLink
Log in

A Three-Dimensional Reconstruction Integrated System for Brain Multiple Sclerosis Lesions

  • Conference paper
  • First Online:
Computer Analysis of Images and Patterns (CAIP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13052))

Included in the following conference series:

Abstract

In the course of a human brain acquisition, which is acquired by a magnetic resonance imager (MRI), two-dimensional (2D) slices of the brain are captured. These have to be aligned and reconstructed to a three-dimensional (3D) volume, which will better assist the doctor in following up the development of the disease. In this study, a 3D reconstruction integrated system for MRI brain multiple sclerosis (MS) lesion visualization is proposed. Brain MRI images from 5 MS subjects were acquired at four diffident consecutive time points (TP1-TP4) with an interval of 6–12 months. MS lesions were manually segmented by an expert neurologist and semi-automatically by a system and reconstructed in a brain volume. The proposed system assists the doctor in following up the MS disease progression and provides support to better manage the disease. The proposed system includes a 5-stage investigation (pre-processing, lesion segmentation, 3D reconstruction, volume estimation and method evaluation), as well as a module for the quantitative evaluation of the method. Twenty MRI images of the brain were used to evaluate the proposed system. Results show that the 3D reconstruction method proposed in this work, can be used to differentiate brain tissues and recognize MS lesions by providing improved 3D visualization. These preliminary results provide evidence that the proposed system could be applied in the future in clinical practice given that it is further evaluated on more subjects.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html.

  2. 2.

    GLSL stand for OpenGL Shading Language it is a programming language similar to C and it runs directly on the graphics processor unit.

References

  1. Karussis, D.: The diagnosis of multiple sclerosis and the various related demyelinating syndromes: a critical review. J. Autoimmun. 48–49, 134–142 (2014)

    Article  Google Scholar 

  2. Dobson, R., Giovannoni, G.: Multiple sclerosis–a review. Eur. J. Neurol. 26(1), 27–40 (2019)

    Article  Google Scholar 

  3. Eickhoff, S.B., Yeo, B.T.T., Genon, S.: Imaging-based parcellations of the human brain. Nat. Rev. Neurosci. 19(11), 672–686 (2018)

    Article  Google Scholar 

  4. Goldberg-Zimring, D., Azhari, H., Miron, S., Achiron, A.: 3-D surface reconstruction of multiple sclerosis lesions using spherical harmonics. Magn. Reson. Med. 46(4), 756–766 (2001)

    Article  Google Scholar 

  5. Cordovez, M.J., Gálvez, G.M., Rojas, C.G., Bravo, C.C., Cerda, E.C.: Use of lesion volumes and loads for monitoring patients with multiple sclerosis. Local experience and literature review. Rev. Chil. Radiol. 19(4), 156–164 (2013)

    Google Scholar 

  6. Loizou, C.P., Papacharalambous, C., Samaras, G., Kyriacou, E., et al.: brain image and lesions registration and 3d reconstruction in dicom mri images. In: Proceedings of 30th IEEE Symposium Computer Based Medical Systems, pp. 419–422 (2017)

    Google Scholar 

  7. Fischl, B.: FreeSurfer. Neuroimage 62(2), 774–781 (2012)

    Article  Google Scholar 

  8. McAuliffe, M.J., Lalonde, F.M., McGarry, D., Gandler, W., et al.: Medical image processing, analysis & visualization in clinical research. In: Proceedings of 14th IEEE Symposium Computer Based Medical System, pp. 381–388 (2001)

    Google Scholar 

  9. Georgiou, A., Loizou, C.P., Nicolaou A., Pantzaris M., Pattichis, C.S.: An adaptive semi-automated integrated system for multiple sclerosis lesion segmentation in longitudinal mri scans based on a convolutional neural network. In Proceedings of Computer Analysis of Images and Patterns, 19th International Conference, CAIP 2021, This volume, (2021)

    Google Scholar 

  10. Campagnola, L., Klein, A., Larson, E., Rossant, C., et al.: VisPy: harnessing the GPU for fast, high-level visualization. In: Proceedings of 14th Python in Science Conference, pp. 93–99. HAL, Texas (2015)

    Google Scholar 

  11. Harris, C.R., Millman, K.J., van der Walt, S.J., Gommers, R., et al.: Array programming with NumPy. Nature 585(7825), 357–362 (2020)

    Article  Google Scholar 

  12. Kaehler, A., Bradski, G.: Learning OpenCV3: Computer Vision in C++ with the OpenCV library. Sebastopol (2016)

    Google Scholar 

  13. Virtanen, P., et al.: SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17(3), 261–272 (2020). https://doi.org/10.1038/s41592-019-0686-2

    Article  Google Scholar 

  14. Carass, A., Roy, S., Jog, A., Cuzzocreo, J.L., et al.: Longitudinal multiple sclerosis lesion segmentation: resource and challenge. Neuroimage 148, 77–102 (2017)

    Article  Google Scholar 

  15. Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imag. 29(6), 1310–1320 (2010)

    Article  Google Scholar 

  16. Shiee, N., Bazin, P.-L., Cuzzocreo, J.L., Ye, C., et al.: Reconstruction of the human cerebral cortex robust to white matter lesions: method & validation. Hum. Brain Mapp. 35(7), 3385–3401 (2014)

    Article  Google Scholar 

  17. Carass, A., Wheeler, M.B., Cuzzocreo, J., Bazin, P.L., et al.: A joint registration and segmentation approach to skull stripping. In: Proceedings of 4th IEEE International Symposium Biomedical Imaging, pp. 656–659 (2007)

    Google Scholar 

  18. Ibtehaz, N., Rahman, M.S.: MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neur. Networks 121, 74–87 (2020)

    Article  Google Scholar 

  19. Dougherty, G. (ed.): Medical Image Processing. Springer, New York (2011). https://doi.org/10.1007/978-1-4419-9779-1

    Book  Google Scholar 

  20. Filippi, M., Rocca, M.A., Ciccarelli, O., De Stefano, N., et al.: MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. Lancet Neurol. 15(3), 292–303 (2016)

    Article  Google Scholar 

  21. Loizou, C.P., Pantzaris, M., Pattichis, C.S.: Normal appearing brain white matter changes in relapsing multiple sclerosis: texture image and classification analysis in serial MRI scans. Magn. Reson. Imaging 73, 192–202 (2020)

    Article  Google Scholar 

  22. Vishnevskiy, V., Walheim, J., Korke, S.: Deep variational network for rapid 4D flow MRI reconstruction. Nat. Mach. Intell. 2(4), 228–235 (2020)

    Article  Google Scholar 

  23. Zhou, B., Zhou, K.S.: DuDoRNet: learning a dual-domain recurrent network for fast MRI reconstruction with deep T1 prior. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4273–4282 (2020)

    Google Scholar 

  24. Nicolaou A., Loizou, C.P., Pantzaris M., Kakas A., Pattichis, C.S.: Rule extraction in the assessment of brain MRI lesions in multiple sclerosis: preliminary findings. In Proceeding of Computer Analysis of Images and Patterns, 19th International Conference, CAIP 2021, This volume, (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Engineering and Informatics, Cyprus University of Technology, 3040, Limassol, Cyprus

    Charalambos Gregoriou, Christos P. Loizou & Andreas Georgiou

  2. Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus

    Marios Pantzaris

  3. Department of Computer Science, University of Cyprus, 1 Panepistimou Avenue, 2109, Aglantzia, Nicosia, Cyprus

    Constantinos S. Pattichis

Authors
  1. Charalambos Gregoriou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christos P. Loizou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andreas Georgiou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marios Pantzaris
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Constantinos S. Pattichis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Charalambos Gregoriou .

Editor information

Editors and Affiliations

  1. Cyprus University of Technology, Limassol, Cyprus

    Nicolas Tsapatsoulis

  2. University of Cyprus, Nicosia, Cyprus

    Andreas Panayides

  3. University of Cyprus, Nicosia, Cyprus

    Theo Theocharides

  4. Cyprus University of Technology, Limassol, Cyprus

    Andreas Lanitis

  5. University of Cyprus, Nicosia, Cyprus

    Constantinos Pattichis

  6. University of Salerno, Salerno, Italy

    Mario Vento

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gregoriou, C., Loizou, C.P., Georgiou, A., Pantzaris, M., Pattichis, C.S. (2021). A Three-Dimensional Reconstruction Integrated System for Brain Multiple Sclerosis Lesions. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13052. Springer, Cham. https://doi.org/10.1007/978-3-030-89128-2_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-89128-2_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89127-5

  • Online ISBN: 978-3-030-89128-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation