Semi-automatic Crohn’s Disease Severity Estimation on MR Imaging

  • Peter J. Schüffler
  • Dwarikanath Mahapatra
  • Robiel Naziroglu
  • Zhang Li
  • Carl A. J. Puylaert
  • Rado Andriantsimiavona
  • Franciscus M. Vos
  • Doug A. Pendsé
  • C. Yung Nio
  • Jaap Stoker
  • Stuart A. Taylor
  • Joachim M. Buhmann
Conference paper

DOI: 10.1007/978-3-319-13692-9_12

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8676)
Cite this paper as:
Schüffler P.J. et al. (2014) Semi-automatic Crohn’s Disease Severity Estimation on MR Imaging. In: Yoshida H., Näppi J., Saini S. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2014. Lecture Notes in Computer Science, vol 8676. Springer, Cham

Abstract

Crohn’s disease (CD) is a chronic inflammatory bowel disease which can be visualized by magnetic resonance imaging (MRI). For CD grading, several non-invasive MRI based severity scores are known, most prominent the MaRIA and AIS. As these scores rely on manual MRI readings for individual bowel segments by trained radiologists, automated MRI assessment has been more and more focused in recent research. We show on a dataset of 27 CD patients that semi-automatically measured bowel wall thickness (ABWT) and dynamic contrast enhancement (DCE) completely outperform manual scorings: the segmental correlation to the Crohn’s Disease Endoscopic Index of Severity (CDEIS) of ABWT and DCE is significantly higher (r = .78) than that of MaRIA (r = .45) or AIS (r = .51). Also on a per-patient basis, the models with ABWT and DCE show significantly higher correlation (r = .69) to global CDEIS than MaRIA (r = .46).

Keywords

Computer vision Crohn’s disease Crohn’s disease severity MRI 

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Peter J. Schüffler
    • 1
  • Dwarikanath Mahapatra
    • 1
  • Robiel Naziroglu
    • 2
  • Zhang Li
    • 2
  • Carl A. J. Puylaert
    • 3
  • Rado Andriantsimiavona
    • 4
  • Franciscus M. Vos
    • 2
    • 3
  • Doug A. Pendsé
    • 5
  • C. Yung Nio
    • 3
  • Jaap Stoker
    • 3
  • Stuart A. Taylor
    • 5
    • 6
  • Joachim M. Buhmann
    • 1
  1. 1.Department of Computer ScienceETH ZurichZurichSwitzerland
  2. 2.Quantitative Imaging GroupTU DelftDelftThe Netherlands
  3. 3.Department of RadiologyAMCAmsterdamThe Netherlands
  4. 4.Biotronics3D LtdLondonUK
  5. 5.Centre for Medical ImagingUCLLondonUK
  6. 6.Department of RadiologyUCLHLondonUK

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