Skip to main content
SpringerLink
Log in

An Adaptive Semi-automated Integrated System for Multiple Sclerosis Lesion Segmentation in Longitudinal MRI Scans Based on a Convolutional Neural Network

  • 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

This work proposes and evaluates a semi-automated integrated segmentation system for multiple sclerosis (MS) lesions in fluid-attenuated inversion recovery (FLAIR) brain magnetic resonance images (MRI). The proposed system uses an adaptive two-dimensional (2D) full convolutional neural network (CNN) and is applied to each MRI brain slice separately. The system is based on a U-Net architecture and allows manual error corrections by the user. This task produces continuing additional improvements to the accuracy of the segmentation system, which can be adapted and reconfigured interactively based on the data entered by the user of the system. The system was evaluated based on the ISBI dataset, on 20 MRI brain images acquired from 5 MS subjects who repeated their examinations in four consecutive time points (TP1-TP4). Manual lesion delineations were provided by two different experts. A Dice Similarity Coefficient (DSC) of 0.76 was achieved using the proposed system which is the highest achieved also by another system. A higher DSC of 0.82 was achieved when the proposed system was evaluated on TP4 images only. A larger dataset will be analyzed in the future, and new measurement metrics will be suggested.

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://www.python.org/;

  2. 2.

    https://www.tensorflow.org/.

  3. 3.

    https://keras.io/.

References

  1. Trip, S.A., Miller, D.H.: Imaging in multiple sclerosis. Neurol. Pract. 76(3), 11–19 (2005)

    Google Scholar 

  2. Gross, H.J., Watson, C.: Characteristics, burden of illness, and physical functioning of patients with relapsing-remitting and secondary progressive multiple sclerosis: a cross-sectional US survey. Neuropsychiatr. Dis. Treat. 13, 1349–1357 (2017)

    Article  Google Scholar 

  3. Loizou, C.P., Petroudi, S., Seimenis, I., Pantziaris, M., et al.: Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome. J Neuroradiol 42(2), 99–114 (2015)

    Article  Google Scholar 

  4. 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(August), 192–202 (2020)

    Article  Google Scholar 

  5. Wicks, D.A.G., Tofts, P.S., Miller, D.H., de Boulay, G.H., et al.: Volume measurement of multiple sclerosis lesions with magnetic resonance images - a preliminary study. Neuroradiology 34(6), 475–479 (1992)

    Article  Google Scholar 

  6. Polman, C.H., Reingold, S.C., Banwell, B., Clanet, M., et al.: Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69(2), 292–302 (2011)

    Article  Google Scholar 

  7. Cabezas, M., Oliver, A., Valverde, S., Beltran, B., et al.: BOOST: a supervised approach for multiple sclerosis lesion segmentation. J. Neurosci. Methods 237, 108–117 (2014)

    Article  Google Scholar 

  8. 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 

  9. Jesson, A., Arbel, T.: Hierarchical MRF and random forest segmentation of MS lesions and healthy tissues in brain MRI (2015)

    Google Scholar 

  10. Maier, O., Handels, H.: MS lesion segmentation in MRI with random forests. In: Proc 2015 Longitudinal Multiple Sclerosis Lesion Segmentation Challenge, pp. 5–6 (2015)

    Google Scholar 

  11. Weng, W., Zhu, X.: INet: convolutional networks for biomedical image segmentation. IEEE Access 9, 16591–16603 (2021)

    Article  Google Scholar 

  12. Brosch, T., Tang, L.Y.W.W., Yoo, Y., Li, D.K.B.B., et al.: Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE Trans. Med. Imaging 35(5), 1229–1239 (2016)

    Article  Google Scholar 

  13. Aslani, S., Dayan, M., Storelli, L., Filippi, M., et al.: Multi-branch convolutional neural network for multiple sclerosis lesion segmentation. Neuroimage 196(March), 1–15 (2019)

    Article  Google Scholar 

  14. Afzal, H.M.R., Luo, S., Ramadan, S., Lechner-Scott, J., et al.: Automatic and robust segmentation of multiple sclerosis lesions with convolutional neural networks. Comput. Mater. Contin. 66(1), 977–991 (2021)

    Article  Google Scholar 

  15. Vaidya, S., Chunduru, A., Muthuganapathy, R., Krishnamurthi, G.: Longitudinal multiple sclerosis lesion segmentation using 3D convolutional neural networks (2015)

    Google Scholar 

  16. Narayana, P.A., Coronado, I., Sujit, S.J., Wolinsky, J.S., Lublin, F.D., Gabr, R.E.: Deep‐learning‐based neural tissue segmentation of MRI in multiple sclerosis: effect of training set size. J. Magn. Reson. Imaging 51(5), 1487–1496 (2019). https://doi.org/10.1002/jmri.26959

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Carass, A., Wheeler, M.B., Cuzzocreo, J., Bazin, P., et al.: Image analysis and communications laboratory, electrical and computer engineering. In: Division of Psychiatric Neuroimaging, Psychiatry and Behavioral Sciences, MedIC, Neuroradiology Division, Radiology and Radiological Science, The Johns Hopkins University Library (London), pp. 656–659 (2007)

    Google Scholar 

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

    Article  Google Scholar 

  20. Rafael C. González, R.E.W.: Digital Image Processing. Prentice Hall (2007)

    Google Scholar 

  21. Wang, J., Perez, L.: The Effectiveness of data augmentation in image classification using deep learning (2017)

    Google Scholar 

  22. Sweeney, E.M., Shinohara, R.T., Reich, D.S., Crainiceanu, C.M., et al.: Automatic lesion incidence estimation and detection in multiple sclerosis using. AJNR Am. J. Neuroradiol. 34(1), 68–73 (2013)

    Article  Google Scholar 

  23. Styner, M., Lee, J., Chin, B., Chin, M.S., et al.: 3D Segmentation in the clinic: a grand challenge II: MS lesion segmentation (2008)

    Google Scholar 

  24. Zijdenbos, A.P., Dawant, B.M., Margolin, R.A., Palmer, A.C.: Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans. Med. Imaging 13(4), 716–724 (1994)

    Article  Google Scholar 

  25. Molyneux, P.D.: Precision and reliability for measurement of change in MRI lesion volume in multiple sclerosis: a comparison of two computer assisted techniques. J. Neurol. Neurosurg. Psychiat. 65(1), 42–47 (1998)

    Article  Google Scholar 

  26. Gregoriou C., Loizou, C.P., Georgiou A., Pantzaris M., Pattichis, C.S.: A Three-dimensional reconstruction integrated system for brain multiple sclerosis lesions. In: Proceedings of Computer Analysis of Images and Patterns, 19th International Conference, CAIP 2021, This volume (2021)

    Google Scholar 

  27. 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: Proceedings 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, Saripolou Street, 3040, Limassol, Cyprus

    Andreas Georgiou & Christos P. Loizou

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

    Andria Nicolaou & Constantinos S. Pattichis

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

    Marios Pantzaris

Authors
  1. Andreas Georgiou
    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. Andria Nicolaou
    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 Andreas Georgiou .

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

Georgiou, A., Loizou, C.P., Nicolaou, A., Pantzaris, M., Pattichis, C.S. (2021). An Adaptive Semi-automated Integrated System for Multiple Sclerosis Lesion Segmentation in Longitudinal MRI Scans Based on a Convolutional Neural Network. 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_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-89128-2_25

  • 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