@inproceedings{09f81631053b4faab34a40c6ebe272d1,
title = "A model development pipeline for Crohn's disease severity assessment from magnetic resonance images",
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 = "AIS, CDEIS, Crohn's Disease, MaRIA, abdominal MRI",
author = "Sch{\"u}ffler, {Peter J.} and Dwarikanath Mahapatra and Tielbeek, {Jeroen A.W.} and Vos, {Franciscus M.} and Jesica Makanyanga and Pends{\'e}, {Doug A.} and Nio, {C. Yung} and Jaap Stoker and Taylor, {Stuart A.} and Buhmann, {Joachim M.}",
year = "2013",
doi = "10.1007/978-3-642-41083-3_1",
language = "English",
isbn = "9783642410826",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "1--10",
booktitle = "Abdominal Imaging",
note = "5th International Workshop on Abdominal Imaging: Computation and Clinical Applications, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 ; Conference date: 22-09-2013 Through 22-09-2013",
}