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


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

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

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. More... »

PAGES

1-10

References to SciGraph publications

Book

TITLE

Abdominal Imaging. Computation and Clinical Applications

ISBN

978-3-642-41082-6
978-3-642-41083-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-41083-3_1

DOI

http://dx.doi.org/10.1007/978-3-642-41083-3_1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1034816957


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