SpringerLink
Log in

Rule Extraction in the Assessment of Brain MRI Lesions in Multiple Sclerosis: Preliminary Findings

  • 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

Various artificial intelligence (AI) algorithms have been proposed in the literature, that are used as medical assistants in clinical diagnostic tasks. Explainability methods are lighting the black-box nature of these algorithms. The objective of this study was the extraction of rules for the assessment of brain magnetic resonance imaging (MRI) lesions in Multiple Sclerosis (MS) subjects based on texture features. Rule extraction of lesion features was used to explain and provide information on the disease diagnosis and progression. A dataset of 38 subjects diagnosed with a clinically isolated syndrome (CIS) of MS and MRI detectable brain lesions were scanned twice with an interval of 6–12 months. MS lesions were manually segmented by an experienced neurologist. Features were extracted from the segmented MS lesions and were correlated with the expanded disability status scale (EDSS) ten years after the initial diagnosis in order to quantify future disability progression. The subjects were separated into two different groups, G1: EDSS ≤ 3.5 and G2: EDSS > 3.5. Classification models were implemented on the KNIME analytics platform using decision trees (DT), to estimate the models with high accuracy and extract the best rules. The results of this study show the effectiveness of rule extraction as it can differentiate MS subjects with benign course of the disease (G1: EDSS ≤ 3.5) and subjects with advanced accumulating disability (G2: EDSS > 3.5) using texture features. Further work is currently in progress to incorporate argumentation modeling to enable rule combination as well as better explainability. The proposed methodology should also be 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

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Filippi, M., Rocca, M.A.: MRI evidence for multiple sclerosis as a diffuse disease of the central nervous system. J. Neurol. 252, 16–24 (2005)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Thompson, A.J., Banwell, B.L., Barkhof, F., Carroll, W.M., et al.: Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 17, 162–173 (2018)

    Article  Google Scholar 

  4. McDonald, W.I., Compston, A., Edan, G., Goodkin, D., et al.: Recommended diagnostic criteria for multiple sclerosis: guidelines from the international panel on the diagnosis of multiple sclerosis. Ann. Neurol. 50, 121–127 (2001)

    Article  Google Scholar 

  5. Castellano, G., Bonilha, L., Li, L.M., Cendes, F.: Texture analysis of medical images. Clin. Radiol. 59, 1061–1069 (2004)

    Article  Google Scholar 

  6. Zhang, Y.: MRI texture analysis in multiple sclerosis. Int. J. Biomed. Imaging 2012, 1–7 (2012)

    Google Scholar 

  7. Harrison, L.C.V., et al.: MRI texture analysis in multiple sclerosis: toward a clinical analysis protocol. Acad. Radiol. 17, 696–707 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. 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 

  10. Weber, C.E., Wittayer, M., Kraemer, M., Dabringhaus, A., et al.: Quantitative MRI texture analysis in chronic active multiple sclerosis lesions. Magn. Reson. Imaging 79, 97–102 (2021)

    Article  Google Scholar 

  11. Singh, A., Sengupta, S., Lakshminarayanan, V.: Explainable deep learning models in medical image analysis. J. Imaging 6, 1–19 (2020)

    Article  Google Scholar 

  12. Holzinger, A., Biemann, C., Pattichis, C.S., Kell, D.B.: What do we need to build explainable AI systems for the medical domain? ar**v, 1–28 (2017)

    Google Scholar 

  13. Windisch, P., et al.: Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices. Neuroradiology 62(11), 1515–1518 (2020). https://doi.org/10.1007/s00234-020-02465-1

    Article  Google Scholar 

  14. Achilleos, K.G., Leandrou, S., Prentzas, N., Kyriacou, P.A. et al.: Extracting explainable assessments of Alzheimer’s disease via machine learning on brain MRI imaging data. In: 2020 IEEE 20th International Conference of Bioinformatics and Bioengineering BIBE, pp. 1036–1041 (2020)

    Google Scholar 

  15. Prentzas, N., Nicolaides, A., Kyriacou, E., Kakas, A., Pattichis C.: Integrating machine learning with symbolic reasoning to build an explainable AI model for stroke prediction. In: 2019 IEEE 19th International Conference of Bioinformatics and Bioengineering BIBE, pp. 817–821 (2019)

    Google Scholar 

  16. Kurtzke, J.F.: Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33, 1444–1452 (1983)

    Article  Google Scholar 

  17. Meier, D.S., Guttmann, C.R.G.: Time-series analysis of MRI intensity patterns in multiple sclerosis. Neuroimage 20, 1193–1209 (2003)

    Article  Google Scholar 

  18. Widmann, M., Roccato, A.: From modeling to model evaluation, 1st edn. KNIME Press, Switzerland (2021)

    Google Scholar 

  19. Silipo, R.: Practicing data science, 3rd edn. KNIME Press, Switzerland (2021)

    Google Scholar 

  20. Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manag. Process 5(2), 1–11 (2015)

    Google Scholar 

  21. Gregoriou, C., Loizou, C.P., Georgiou, A., Pantzaris, M., Pattichis, C.S.: A three-dimensional reconstruction integrated system for brain multiple sclerosis lesions. In: 19th International Conference of Computation Analysis of Images Patterns CAIP (2021)

    Google Scholar 

  22. 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: 19th International Conference of Computation Analysis of Images Patterns CAIP (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    Andria Nicolaou, Antonis Kakas & Constantinos S. Pattichis

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

    Christos P. Loizou

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

    Marios Pantzaris

Authors
  1. Andria Nicolaou
    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. Marios Pantzaris
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Antonis Kakas
    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 Andria Nicolaou .

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

Nicolaou, A., Loizou, C.P., Pantzaris, M., Kakas, A., Pattichis, C.S. (2021). Rule Extraction in the Assessment of Brain MRI Lesions in Multiple Sclerosis: Preliminary Findings. 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_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-89128-2_27

  • 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