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

  • Peter J. Schüffler
  • Dwarikanath Mahapatra
  • Jeroen A. W. Tielbeek
  • Franciscus M. Vos
  • Jesica Makanyanga
  • Doug A. Pendsé
  • C. Yung Nio
  • Jaap Stoker
  • Stuart A. Taylor
  • Joachim M. Buhmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8198)

Abstract

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.

Keywords

Crohn’s Disease abdominal MRI CDEIS MaRIA AIS 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Peter J. Schüffler
    • 1
  • Dwarikanath Mahapatra
    • 1
  • Jeroen A. W. Tielbeek
    • 2
  • Franciscus M. Vos
    • 2
    • 3
  • Jesica Makanyanga
    • 4
  • Doug A. Pendsé
    • 4
  • C. Yung Nio
    • 2
  • Jaap Stoker
    • 2
  • Stuart A. Taylor
    • 4
    • 5
  • Joachim M. Buhmann
    • 1
  1. 1.Dept of Computer ScienceETH ZurichZurichSwitzerland
  2. 2.Dept of RadiologyAcademic Medical CenterAmsterdamThe Netherland
  3. 3.Quantitative Imaging GroupDelft University of TechnologyDelftThe Netherland
  4. 4.Centre for Medical ImagingUniversity College LondonLondonUK
  5. 5.Dept of RadiologyUniversity College Hospital LondonLondonUK

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