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A Model Development Pipeline for Crohn’s Disease Severity Assessment from Magnetic Resonance Images

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 8198)


Crohn’s Disease affects the intestinal tract of a patient and can have varying severity which influences treatment strategy. The clinical severity score CDEIS (Crohn’s Disease Endoscopic Index of severity) ranges from 0 to 44 and is measured by endoscopy. In this paper we investigate the potential of non-invasive magnetic resonance imaging to assess this severity, together with the underlying question which features are most relevant for this estimation task. We propose a new general and modular pipeline that uses machine learning techniques to quantify disease severity from MR images and show its value on Crohn’s Disease severity assessment on 30 patients scored by 4 medical experts. With the pipeline, we can obtain a magnetic resonance imaging score which outperforms two existing reference scores MaRIA and AIS.


  • Crohn’s Disease
  • abdominal MRI
  • MaRIA
  • AIS

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  1. Rimola, J., Ordas, I., Rodriguez, S., Garcia-Bosch, O., Aceituno, M., Llach, J., Ayuso, C., Ricart, E., Panes, J.: Magnetic resonance imaging for evaluation of Crohn’s disease: validation of parameters of severity and quantitative index of activity. Inflammatory Bowel Diseases 17, 1759–1768 (2011)

    CrossRef  Google Scholar 

  2. Steward, M.J., Punwani, S., Proctor, I., Adjei-Gyamfi, Y., Chatterjee, F., Bloom, S., Novelli, M., Halligan, S., Rodriguez-Justo, M., Taylor, S.A.: Non-perforating small bowel Crohn’s disease assessed by MRI enterography: derivation and histopathological validation of an MR-based activity index. European Journal of Radiology 81, 2080–2088 (2012)

    CrossRef  Google Scholar 

  3. Ziech, M.L., Lavini, C., Caan, M.W., Nio, C.Y., Stokkers, P.C., Bipat, S., Ponsioen, C.Y., Nederveen, A.J., Stoker, J.: Dynamic contrast-enhanced MRI in patients with luminal Crohn’s disease. European Journal of Radiology 81, 3019–3027 (2012)

    CrossRef  Google Scholar 

  4. Tielbeek, J.A.W., Makanyanga, J.C., Bipat, S., Pendsé, D.A., Yung Nio, C., Vos, F.M., Taylor, S.A., Stoker, J.: Grading Crohn’s disease activity with MRI: Interobserver variability of MRI features, MRI scoring of severity and correlation with Crohn’s Disease Endoscopic Index of Severity. AJR (2013)

    Google Scholar 

  5. Mahapatra, D., Schueffler, P., Tielbeek, J., Vos, F.M., Buhmann, J.M.: Crohn’s Disease Tissue Segmentation from Abdominal MRI Using Semantic Information and Graph Cuts. In: Proc. IEEE ISBI 2013, San Francisco, pp. 358–361 (2013)

    Google Scholar 

  6. Mahapatra, D., Schueffler, P., Tielbeek, J.A.W., Buhmann, J.M., Vos, F.M.: A Supervised Learning Based Approach to Detect Crohn’s Disease in Abdominal MR Volumes. In: Yoshida, H., Hawkes, D., Vannier, M.W. (eds.) Abdominal Imaging 2012. LNCS, vol. 7601, pp. 97–106. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  7. Hastie, T., Tibshirani, R., Friedman, J.H.: The elements of statistical learning: data mining, inference, and prediction. Springer, New York (2009)

    CrossRef  Google Scholar 

  8. Rimola, J., Rodriguez, S., Garcia-Bosch, O., Ordas, I., Ayala, E., Aceituno, M., Pellise, M., Ayuso, C., Ricart, E., Donoso, L., Panes, J.: Magnetic resonance for assessment of disease activity and severity in ileocolonic Crohn’s disease. Gut 58, 1113–1120 (2009)

    CrossRef  Google Scholar 

  9. Bonferroni, C.E.: Il calcolo delle assicurazioni su gruppi di teste. In: Studi in Onore del Professore Salvatore Ortu Carboni, Rome, pp. 13–60 (1935)

    Google Scholar 

  10. Cima, I., Schiess, R., Wild, P., Kaelin, M., Schueffler, P., Lange, V., Picotti, P., Ossola, R., Templeton, A., Schubert, O., Fuchs, T., Leippold, T., Wyler, S., Zehetner, J., Jochum, W., Buhmann, J., Cerny, T., Moch, H., Gillessen, S., Aebersold, R., Krek, W.: Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer. Proceedings of the National Academy of Sciences of the United States of America 108, 3342–3347 (2011)

    Google Scholar 

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Schüffler, P.J. et al. (2013). A Model Development Pipeline for Crohn’s Disease Severity Assessment from Magnetic Resonance Images. In: Yoshida, H., Warfield, S., Vannier, M.W. (eds) Abdominal Imaging. Computation and Clinical Applications. ABD-MICCAI 2013. Lecture Notes in Computer Science, vol 8198. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41082-6

  • Online ISBN: 978-3-642-41083-3

  • eBook Packages: Computer ScienceComputer Science (R0)