Computational Radiology and Crohn's Disease

Crohn's disease (CD) is a chronic inflammatory bowel disease which can affect the terminal ileum and/or one or more parts of the colon. Longterm monitoring of this disorder is necessary for appropriate treatment and for research. Automatic detection and scoring of CD with magnetic resonance imaging (MRI) would substantially facilate and improve personalized and objective screening.

We make effort in automatic methods in detecting and segmenting CD in MRI as well as grading CD for scoring its severity. This project has been initiated by the European joint project VIGOR++.

Crohn's Disease Grading Pipeline

Crohn's Disease Scoring

Severity of Crohn's disease is typically manually assessed by colonoscopy, evolving the CD endoscopic index of severity (CDEIS). The information gain to this segmental score by MRI is a research area of the VIGOR++ project. In the same project, the automatic severity assessment by state-of-the-art computer vision techniques is explored.

Contact: Peter J. Schüffler

Collaboration: VIGOR++


Crohn's Disease Segmentation

Crohn's Disease Segmentation

Magnetic resonance imaging (MRI) allows for inspection of signs of disease which are hidden by colonoscopy. The automatic detection and segmentation of CD in MRI has been studied in the VIGOR++ project. The registration of MRI with genetic alterations in CD patients will be a next step for a more holistic view on the patient and better treatment.

Contact: Peter J. Schüffler

Collaboration: VIGOR++

Crohn's Disease
Semi-Automatic Crohn's Disease Severity Estimation on MR Imaging.
P. J. Schüffler, D. Mahapatra, R. E. Naziroglu, Z. Li, C. A. J. Puylaert, R. Andriantsimiavona, F. M. Vos, D. A. Pendsé, C. Yung Nio, J. Stoker, S. A. Taylor and J. M. Buhmann.
6th MICCAI Workshop on Abdominal Imaging - Computational and Clinical Applications, 2014
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@inproceedings{schuffler_semi-automatic_2014,
    title = {Semi-{Automatic} {Crohn}'s {Disease} {Severity} {Estimation} on {MR} {Imaging}},
    url = {http://link.springer.com/chapter/10.1007%2F978-3-319-13692-9_12},
    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).},
    author = {Sch\"uffler, P. J. and Mahapatra, D. and Naziroglu, R. E. and Li, Z. and Puylaert, C. A. J. and Andriantsimiavona, R. and Vos, F. M. and Pends\'e, D. A. and Nio, C. Yung and Stoker, J. and Taylor, S. A. and Buhmann, J. M.},
    year = {2014},
}
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TY - CONF
TI - Semi-Automatic Crohn's Disease Severity Estimation on MR Imaging
AU - Schüffler, P. J.
AU - Mahapatra, D.
AU - Naziroglu, R. E.
AU - Li, Z.
AU - Puylaert, C. A. J.
AU - Andriantsimiavona, R.
AU - Vos, F. M.
AU - Pendsé, D. A.
AU - Nio, C. Yung
AU - Stoker, J.
AU - Taylor, S. A.
AU - Buhmann, J. M.
T2 - 6th MICCAI Workshop on Abdominal Imaging - Computational and Clinical Applications
AB - 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).
DA - 2014///
PY - 2014
UR - http://link.springer.com/chapter/10.1007%2F978-3-319-13692-9_12
ER -
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).
Computer Aided Crohn's Disease Severity Assessment in MRI.
Peter J. Schüffler, Dwarikanath Mahapatra, Franciscus M. Vos and Joachim M. Buhmann.
VIGOR++ Workshop 2014 - Showcase of Research Outcomes and Future Outlook, 2014
Best Poster Award
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@misc{schuffler_computer_2014,
    address = {London},
    type = {Poster},
    title = {Computer {Aided} {Crohn}'s {Disease} {Severity} {Assessment} in {MRI}},
    url = {https://www.researchgate.net/publication/262198303_Computer_Aided_Crohns_Disease_Severity_Assessment_in_MRI},
    author = {Sch\"uffler, Peter J. and Mahapatra, Dwarikanath and Vos, Franciscus M. and Buhmann, Joachim M.},
    year = {2014},
}
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TY - SLIDE
TI - Computer Aided Crohn's Disease Severity Assessment in MRI
T2 - VIGOR++ Workshop 2014 - Showcase of Research Outcomes and Future Outlook
A2 - Schüffler, Peter J.
A2 - Mahapatra, Dwarikanath
A2 - Vos, Franciscus M.
A2 - Buhmann, Joachim M.
CY - London
DA - 2014///
PY - 2014
M3 - Poster
UR - https://www.researchgate.net/publication/262198303_Computer_Aided_Crohns_Disease_Severity_Assessment_in_MRI
ER -
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 and Joachim M. Buhmann.
In: Hiroyuki Yoshida, Simon Warfield and Michael Vannier (eds.) Abdominal Imaging. Computation and Clinical Applications, vol. 8198, p. 1-10, Lecture Notes in Computer Science, Springer Berlin Heidelberg, ISBN 978-3-642-41082-6, 2013
Outstanding Paper Award
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@incollection{schuffler_model_2013,
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {A {Model} {Development} {Pipeline} for {Crohn}'s {Disease} {Severity} {Assessment} from {Magnetic} {Resonance} {Images}},
    volume = {8198},
    isbn = {978-3-642-41082-6},
    url = {http://link.springer.com/chapter/10.1007%2F978-3-642-41083-3_1},
    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.},
    booktitle = {Abdominal {Imaging}. {Computation} and {Clinical} {Applications}},
    publisher = {Springer Berlin Heidelberg},
    author = {Sch\"uffler, Peter J. and Mahapatra, Dwarikanath and Tielbeek, Jeroen A. W. and Vos, Franciscus M. and Makanyanga, Jesica and Pends\'e, Doug A. and Nio, C. Yung and Stoker, Jaap and Taylor, Stuart A. and Buhmann, Joachim M.},
    editor = {Yoshida, Hiroyuki and Warfield, Simon and Vannier, Michael},
    year = {2013},
    keywords = {AIS, CDEIS, Crohn’s Disease, MaRIA, abdominal MRI},
    pages = {1--10}
}
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TY - CHAP
TI - A Model Development Pipeline for Crohn's Disease Severity Assessment from Magnetic Resonance Images
AU - Schüffler, Peter J.
AU - Mahapatra, Dwarikanath
AU - Tielbeek, Jeroen A. W.
AU - Vos, Franciscus M.
AU - Makanyanga, Jesica
AU - Pendsé, Doug A.
AU - Nio, C. Yung
AU - Stoker, Jaap
AU - Taylor, Stuart A.
AU - Buhmann, Joachim M.
T2 - Abdominal Imaging. Computation and Clinical Applications
A2 - Yoshida, Hiroyuki
A2 - Warfield, Simon
A2 - Vannier, Michael
T3 - Lecture Notes in Computer Science
AB - 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.
DA - 2013///
PY - 2013
VL - 8198
SP - 1
EP - 10
PB - Springer Berlin Heidelberg
SN - 978-3-642-41082-6
UR - http://link.springer.com/chapter/10.1007%2F978-3-642-41083-3_1
KW - AIS
KW - CDEIS
KW - Crohn’s Disease
KW - MaRIA
KW - abdominal MRI
ER -
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 Segmentation from MRI Using Learned Image Priors.
D. Mahapatra, P. J. Schüffler, F. M. Vos and J. M. Buhmann.
Proceedings IEEE ISBI 2015, p. 625-628, 2015
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@article{mahapatra_crohns_2015,
    title = {Crohn's {Disease} {Segmentation} from {MRI} {Using} {Learned} {Image} {Priors}},
    url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7163951},
    abstract = {We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.},
    journal = {Proceedings IEEE ISBI 2015},
    author = {Mahapatra, D. and Sch\"uffler, P. J. and Vos, F. M. and Buhmann, J. M.},
    year = {2015},
    pages = {625--628}
}
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TY - JOUR
TI - Crohn's Disease Segmentation from MRI Using Learned Image Priors
AU - Mahapatra, D.
AU - Schüffler, P. J.
AU - Vos, F. M.
AU - Buhmann, J. M.
T2 - Proceedings IEEE ISBI 2015
AB - We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.
DA - 2015///
PY - 2015
SP - 625
EP - 628
UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7163951
ER -
We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.
Automatic Detection and Segmentation of Crohn's Disease Tissues from Abdominal MRI.
D. Mahapatra, P. J. Schüffler, J. A. W. Tielbeek, J. C. Makanyanga, J. Stoker, S. A. Taylor, F. M. Vos and J. M. Buhmann.
IEEE Transactions on Medical Imaging, vol. 32, p. 2332-2347, 2013
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@article{mahapatra_automatic_2013,
    title = {Automatic {Detection} and {Segmentation} of {Crohn}'s {Disease} {Tissues} from {Abdominal} {MRI}},
    volume = {32},
    issn = {0278-0062},
    url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6600949},
    doi = {10.1109/TMI.2013.2282124},
    abstract = {We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers. The supervoxels identified as containing diseased tissues define the volume of interest (VOI). All voxels within the VOI are further investigated to segment the diseased region. Probability maps are generated for each voxel using a second set of RF classifiers which give the probabilities of each voxel being diseased, normal or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that our method achieves high segmentation accuracy with Dice metric values of 0.90 ± 0.04 and Hausdorff distance of 7.3 ± 0.8 mm. Semantic information and context features are an integral part of our method and are robust to different levels of added noise.},
    journal = {IEEE Transactions on Medical Imaging},
    author = {Mahapatra, D. and Sch\"uffler, P. J. and Tielbeek, J. A. W. and Makanyanga, J. C. and Stoker, J. and Taylor, S. A. and Vos, F. M. and Buhmann, J. M.},
    year = {2013},
    keywords = {Anisotropic magnetoresistance, Context, Crohn\&\#x2019, Diseases, Entropy, Image segmentation, Radio frequency, content, graph cut, image features, probability maps, random forests, s disease, segmentation, semantic information, shape, supervoxels},
    pages = {2332--2347}
}
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TY - JOUR
TI - Automatic Detection and Segmentation of Crohn's Disease Tissues from Abdominal MRI
AU - Mahapatra, D.
AU - Schüffler, P. J.
AU - Tielbeek, J. A. W.
AU - Makanyanga, J. C.
AU - Stoker, J.
AU - Taylor, S. A.
AU - Vos, F. M.
AU - Buhmann, J. M.
T2 - IEEE Transactions on Medical Imaging
AB - We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers. The supervoxels identified as containing diseased tissues define the volume of interest (VOI). All voxels within the VOI are further investigated to segment the diseased region. Probability maps are generated for each voxel using a second set of RF classifiers which give the probabilities of each voxel being diseased, normal or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that our method achieves high segmentation accuracy with Dice metric values of 0.90 ± 0.04 and Hausdorff distance of 7.3 ± 0.8 mm. Semantic information and context features are an integral part of our method and are robust to different levels of added noise.
DA - 2013///
PY - 2013
DO - 10.1109/TMI.2013.2282124
VL - 32
SP - 2332
EP - 2347
SN - 0278-0062
UR - http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6600949
KW - Anisotropic magnetoresistance
KW - Context
KW - Crohn’
KW - Diseases
KW - Entropy
KW - Image segmentation
KW - Radio frequency
KW - content
KW - graph cut
KW - image features
KW - probability maps
KW - random forests
KW - s disease
KW - segmentation
KW - semantic information
KW - shape
KW - supervoxels
ER -
We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers. The supervoxels identified as containing diseased tissues define the volume of interest (VOI). All voxels within the VOI are further investigated to segment the diseased region. Probability maps are generated for each voxel using a second set of RF classifiers which give the probabilities of each voxel being diseased, normal or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that our method achieves high segmentation accuracy with Dice metric values of 0.90 ± 0.04 and Hausdorff distance of 7.3 ± 0.8 mm. Semantic information and context features are an integral part of our method and are robust to different levels of added noise.
Decision Support in Radiology
Effect of Reader Experience on Variability, Evaluation Time and Accuracy of Coronary Plaque Detection with Computed Tomography Coronary Angiography.
Stefan Saur, Hatem Alkadhi, Paul Stolzmann, Stephan Baumueller, Sebastian Leschka, Hans Scheffel, Lotus Desbiolles, Thomas J. Fuchs, Gabor Szekely and Philippe Cattin.
European Radiology, vol. 20, 7, p. 1599-1606, 2010
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@article{saur_effect_2010,
    title = {Effect of {Reader} {Experience} on {Variability}, {Evaluation} {Time} and {Accuracy} of {Coronary} {Plaque} {Detection} with {Computed} {Tomography} {Coronary} {Angiography}},
    volume = {20},
    issn = {0938-7994},
    url = {http://dx.doi.org/10.1007/s00330-009-1709-7},
    doi = {10.1007/s00330-009-1709-7},
    number = {7},
    journal = {European Radiology},
    author = {Saur, Stefan and Alkadhi, Hatem and Stolzmann, Paul and Baumueller, Stephan and Leschka, Sebastian and Scheffel, Hans and Desbiolles, Lotus and Fuchs, Thomas J. and Szekely, Gabor and Cattin, Philippe},
    year = {2010},
    keywords = {Medicine},
    pages = {1599--1606}
}
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TY - JOUR
TI - Effect of Reader Experience on Variability, Evaluation Time and Accuracy of Coronary Plaque Detection with Computed Tomography Coronary Angiography
AU - Saur, Stefan
AU - Alkadhi, Hatem
AU - Stolzmann, Paul
AU - Baumueller, Stephan
AU - Leschka, Sebastian
AU - Scheffel, Hans
AU - Desbiolles, Lotus
AU - Fuchs, Thomas J.
AU - Szekely, Gabor
AU - Cattin, Philippe
T2 - European Radiology
DA - 2010///
PY - 2010
DO - 10.1007/s00330-009-1709-7
VL - 20
IS - 7
SP - 1599
EP - 1606
SN - 0938-7994
UR - http://dx.doi.org/10.1007/s00330-009-1709-7
KW - Medicine
ER -
Guided Review by Frequent Itemset Mining: Additional Evidence for Plaque Detection.
Stefan C. Saur, Hatem Alkadhi, Lotus Desbiolles, Thomas J. Fuchs, Gabor Szekely and Philippe C. Cattin.
International Journal of Computer Assisted Radiology and Surgery, vol. 4, 3, p. 263–271, 2009
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@article{saur_guided_2009,
    title = {Guided {Review} by {Frequent} {Itemset} {Mining}: {Additional} {Evidence} for {Plaque} {Detection}},
    volume = {4},
    url = {http://dx.doi.org/10.1007/s11548-009-0290-5},
    number = {3},
    journal = {International Journal of Computer Assisted Radiology and Surgery},
    author = {Saur, Stefan C. and Alkadhi, Hatem and Desbiolles, Lotus and Fuchs, Thomas J. and Szekely, Gabor and Cattin, Philippe C.},
    year = {2009},
    pages = {263--271}
}
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TY - JOUR
TI - Guided Review by Frequent Itemset Mining: Additional Evidence for Plaque Detection
AU - Saur, Stefan C.
AU - Alkadhi, Hatem
AU - Desbiolles, Lotus
AU - Fuchs, Thomas J.
AU - Szekely, Gabor
AU - Cattin, Philippe C.
T2 - International Journal of Computer Assisted Radiology and Surgery
DA - 2009///
PY - 2009
VL - 4
IS - 3
SP - 263
EP - 271
UR - http://dx.doi.org/10.1007/s11548-009-0290-5
ER -
Prediction Rules for the Detection of Coronary Artery Plaques: Evidence from Cardiac CT.
Stefan C. Saur, Philippe C. Cattin, Lotus Desbiolles, Thomas J. Fuchs, Gabor Szekely and Hatem Alkadhi.
Investigative Radiology, vol. 44, 8, p. 483-490, 2009
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@article{saur_prediction_2009,
    title = {Prediction {Rules} for the {Detection} of {Coronary} {Artery} {Plaques}: {Evidence} from {Cardiac} {CT}},
    volume = {44},
    url = {http://journals.lww.com/investigativeradiology/Abstract/2009/08000/Prediction_Rules_for_the_Detection_of_Coronary.8.aspx},
    doi = {http://dx.doi.org/10.1097/RLI.0b013e3181a8afc4},
    abstract = {Objectives: To evaluate spatial plaque distribution patterns in coronary arteries based on computed tomography coronary angiography data sets and to express the learned patterns in prediction rules. An application is proposed to use these prediction rules for the detection of initially missed plaques.
    Material and Methods: Two hundred fifty two consecutive patients with chronic coronary artery disease underwent contrast-enhanced dual-source computed tomography coronary angiography for clinical indications. Coronary artery plaques were manually labeled on a 16-segment coronary model and their position (ie, segments and bifurcations) and composition (ie, calcified, mixed, or noncalcified) were noted. The frequent itemset mining algorithm was used to statistically search for plaque distribution patterns. The patterns were expressed as prediction rules: given plaques at certain locations as conditions, a prediction rule gave evidence—with a certain confidence value—for a plaque at another location within the coronary artery tree. Prediction rules with the highest confidence values were evaluated and described. Furthermore, to improve manual plaque detection, all prediction rules were applied on the patient data to search for segments with potentially missed plaques. These segments were then reviewed in a second, guided reading for the existence of plaques. The same number of segments was also determined by a weighted random approach to evaluate the quality of prediction resulting from frequent itemset mining.
    Results: In 200 of 252 (79.4\%) patients, at least one coronary plaque (range, 1–22 plaques) was found. In total 1229 plaques (990 calcified, 80.6\%; 227 mixed, 18.5\%; 12 noncalcified, 1\%) distributed, over 916 coronary segments and 507 vessels were manually labeled. Four plaque distribution patterns were identified: 20.6\% of the patients had no plaques at all; 31.7\% had plaques in the left coronary artery tree; 46.4\% had plaques both in left and right coronary arteries, whereas 1.2\% of the patients had plaques solely in the right coronary artery (RCA). General rules were found predicting plaques in the left anterior descending artery (LAD), given plaques in segments of the RCA or in the left main artery. Further general rules predicted plaques in the LAD, given plaques in the circumflex artery. In the guided review, the segment selection based on the prediction rules from frequent itemset mining performed significantly better (P {\textless} 0.001) than the weighted random approach by revealing 48 initially missed plaques.
    Conclusions: This study demonstrates spatial plaque distribution patterns in coronary arteries as determined with cardiac CT. Use of the frequent itemset mining algorithm yielded rules that predicted plaques at certain sites given plaques at other sites of the coronary artery tree. Use of these prediction rules improved the manual labeling of coronary plaques as initially missed plaques could be predicted with the guided review.},
    number = {8},
    journal = {Investigative Radiology},
    author = {Saur, Stefan C. and Cattin, Philippe C. and Desbiolles, Lotus and Fuchs, Thomas J. and Szekely, Gabor and Alkadhi, Hatem},
    year = {2009},
    pages = {483--490}
}
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TY - JOUR
TI - Prediction Rules for the Detection of Coronary Artery Plaques: Evidence from Cardiac CT
AU - Saur, Stefan C.
AU - Cattin, Philippe C.
AU - Desbiolles, Lotus
AU - Fuchs, Thomas J.
AU - Szekely, Gabor
AU - Alkadhi, Hatem
T2 - Investigative Radiology
AB - Objectives: To evaluate spatial plaque distribution patterns in coronary arteries based on computed tomography coronary angiography data sets and to express the learned patterns in prediction rules. An application is proposed to use these prediction rules for the detection of initially missed plaques.
Material and Methods: Two hundred fifty two consecutive patients with chronic coronary artery disease underwent contrast-enhanced dual-source computed tomography coronary angiography for clinical indications. Coronary artery plaques were manually labeled on a 16-segment coronary model and their position (ie, segments and bifurcations) and composition (ie, calcified, mixed, or noncalcified) were noted. The frequent itemset mining algorithm was used to statistically search for plaque distribution patterns. The patterns were expressed as prediction rules: given plaques at certain locations as conditions, a prediction rule gave evidence—with a certain confidence value—for a plaque at another location within the coronary artery tree. Prediction rules with the highest confidence values were evaluated and described. Furthermore, to improve manual plaque detection, all prediction rules were applied on the patient data to search for segments with potentially missed plaques. These segments were then reviewed in a second, guided reading for the existence of plaques. The same number of segments was also determined by a weighted random approach to evaluate the quality of prediction resulting from frequent itemset mining.
Results: In 200 of 252 (79.4%) patients, at least one coronary plaque (range, 1–22 plaques) was found. In total 1229 plaques (990 calcified, 80.6%; 227 mixed, 18.5%; 12 noncalcified, 1%) distributed, over 916 coronary segments and 507 vessels were manually labeled. Four plaque distribution patterns were identified: 20.6% of the patients had no plaques at all; 31.7% had plaques in the left coronary artery tree; 46.4% had plaques both in left and right coronary arteries, whereas 1.2% of the patients had plaques solely in the right coronary artery (RCA). General rules were found predicting plaques in the left anterior descending artery (LAD), given plaques in segments of the RCA or in the left main artery. Further general rules predicted plaques in the LAD, given plaques in the circumflex artery. In the guided review, the segment selection based on the prediction rules from frequent itemset mining performed significantly better (P < 0.001) than the weighted random approach by revealing 48 initially missed plaques.
Conclusions: This study demonstrates spatial plaque distribution patterns in coronary arteries as determined with cardiac CT. Use of the frequent itemset mining algorithm yielded rules that predicted plaques at certain sites given plaques at other sites of the coronary artery tree. Use of these prediction rules improved the manual labeling of coronary plaques as initially missed plaques could be predicted with the guided review.
DA - 2009///
PY - 2009
DO - http://dx.doi.org/10.1097/RLI.0b013e3181a8afc4
VL - 44
IS - 8
SP - 483
EP - 490
UR - http://journals.lww.com/investigativeradiology/Abstract/2009/08000/Prediction_Rules_for_the_Detection_of_Coronary.8.aspx
ER -
Objectives: To evaluate spatial plaque distribution patterns in coronary arteries based on computed tomography coronary angiography data sets and to express the learned patterns in prediction rules. An application is proposed to use these prediction rules for the detection of initially missed plaques. Material and Methods: Two hundred fifty two consecutive patients with chronic coronary artery disease underwent contrast-enhanced dual-source computed tomography coronary angiography for clinical indications. Coronary artery plaques were manually labeled on a 16-segment coronary model and their position (ie, segments and bifurcations) and composition (ie, calcified, mixed, or noncalcified) were noted. The frequent itemset mining algorithm was used to statistically search for plaque distribution patterns. The patterns were expressed as prediction rules: given plaques at certain locations as conditions, a prediction rule gave evidence—with a certain confidence value—for a plaque at another location within the coronary artery tree. Prediction rules with the highest confidence values were evaluated and described. Furthermore, to improve manual plaque detection, all prediction rules were applied on the patient data to search for segments with potentially missed plaques. These segments were then reviewed in a second, guided reading for the existence of plaques. The same number of segments was also determined by a weighted random approach to evaluate the quality of prediction resulting from frequent itemset mining. Results: In 200 of 252 (79.4%) patients, at least one coronary plaque (range, 1–22 plaques) was found. In total 1229 plaques (990 calcified, 80.6%; 227 mixed, 18.5%; 12 noncalcified, 1%) distributed, over 916 coronary segments and 507 vessels were manually labeled. Four plaque distribution patterns were identified: 20.6% of the patients had no plaques at all; 31.7% had plaques in the left coronary artery tree; 46.4% had plaques both in left and right coronary arteries, whereas 1.2% of the patients had plaques solely in the right coronary artery (RCA). General rules were found predicting plaques in the left anterior descending artery (LAD), given plaques in segments of the RCA or in the left main artery. Further general rules predicted plaques in the LAD, given plaques in the circumflex artery. In the guided review, the segment selection based on the prediction rules from frequent itemset mining performed significantly better (P < 0.001) than the weighted random approach by revealing 48 initially missed plaques. Conclusions: This study demonstrates spatial plaque distribution patterns in coronary arteries as determined with cardiac CT. Use of the frequent itemset mining algorithm yielded rules that predicted plaques at certain sites given plaques at other sites of the coronary artery tree. Use of these prediction rules improved the manual labeling of coronary plaques as initially missed plaques could be predicted with the guided review.
Automatic and Robust Forearm Segmentation Using Graph Cuts.
Philipp Fuernstahl, Thomas J. Fuchs, Andreas Schweizer, Ladislav Nagy, Gabor Szekely and Matthias Harders.
5th IEEE International Symposium on Biomedical Imaging. ISBI 2008, p. 77–80, IEEE, ISBN 978-1-4244-2002-5, 2008
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@inproceedings{fuernstahl_automatic_2008,
    address = {Paris, France},
    title = {Automatic and {Robust} {Forearm} {Segmentation} {Using} {Graph} {Cuts}},
    isbn = {978-1-4244-2002-5},
    url = {http://dx.doi.org/10.1109/ISBI.2008.4540936},
    doi = {10.1109/ISBI.2008.4540936},
    abstract = {The segmentation of bones in computed tomography (CT) images is an important step for the simulation of forearm bone motion, since it allows to include patient specific anatomy in a kinematic model. While the identification of the bone diaphysis is straightforward, the segmentation of bone joints with weak, thin, and diffusive boundaries is still a challenge. We propose a graph cut segmentation approach that is particularly suited to robustly segment joints in 3-d CT images. We incorporate knowledge about intensity, bone shape and local structures into a novel energy function. Our presented framework performs a simultaneous segmentation of both forearm bones without any user interaction.},
    booktitle = {5th {IEEE} {International} {Symposium} on {Biomedical} {Imaging}. {ISBI} 2008},
    publisher = {IEEE},
    author = {Fuernstahl, Philipp and Fuchs, Thomas J. and Schweizer, Andreas and Nagy, Ladislav and Szekely, Gabor and Harders, Matthias},
    year = {2008},
    pages = {77--80}
}
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TY - CONF
TI - Automatic and Robust Forearm Segmentation Using Graph Cuts
AU - Fuernstahl, Philipp
AU - Fuchs, Thomas J.
AU - Schweizer, Andreas
AU - Nagy, Ladislav
AU - Szekely, Gabor
AU - Harders, Matthias
AB - The segmentation of bones in computed tomography (CT) images is an important step for the simulation of forearm bone motion, since it allows to include patient specific anatomy in a kinematic model. While the identification of the bone diaphysis is straightforward, the segmentation of bone joints with weak, thin, and diffusive boundaries is still a challenge. We propose a graph cut segmentation approach that is particularly suited to robustly segment joints in 3-d CT images. We incorporate knowledge about intensity, bone shape and local structures into a novel energy function. Our presented framework performs a simultaneous segmentation of both forearm bones without any user interaction.
C1 - Paris, France
C3 - 5th IEEE International Symposium on Biomedical Imaging. ISBI 2008
DA - 2008///
PY - 2008
DO - 10.1109/ISBI.2008.4540936
SP - 77
EP - 80
PB - IEEE
SN - 978-1-4244-2002-5
UR - http://dx.doi.org/10.1109/ISBI.2008.4540936
ER -
The segmentation of bones in computed tomography (CT) images is an important step for the simulation of forearm bone motion, since it allows to include patient specific anatomy in a kinematic model. While the identification of the bone diaphysis is straightforward, the segmentation of bone joints with weak, thin, and diffusive boundaries is still a challenge. We propose a graph cut segmentation approach that is particularly suited to robustly segment joints in 3-d CT images. We incorporate knowledge about intensity, bone shape and local structures into a novel energy function. Our presented framework performs a simultaneous segmentation of both forearm bones without any user interaction.
Neuroimaging and Connectomics
Neuron Geometry Extraction by Perceptual Grouping in ssTEM Images.
Verena Kaynig, Thomas J. Fuchs and Joachim M. Buhmann.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 0, p. 2902-2909, IEEE Computer Society, ISBN 978-1-4244-6984-0, 2010
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@inproceedings{kaynig_neuron_2010,
    address = {Los Alamitos, CA, USA},
    title = {Neuron {Geometry} {Extraction} by {Perceptual} {Grouping} in {ssTEM} {Images}},
    volume = {0},
    isbn = {978-1-4244-6984-0},
    url = {http://www.computer.org/portal/web/csdl/doi/10.1109/CVPR.2010.5540029},
    doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2010.5540029},
    booktitle = {{IEEE} {Computer} {Society} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}},
    publisher = {IEEE Computer Society},
    author = {Kaynig, Verena and Fuchs, Thomas J. and Buhmann, Joachim M.},
    year = {2010},
    pages = {2902--2909}
}
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TY - CONF
TI - Neuron Geometry Extraction by Perceptual Grouping in ssTEM Images
AU - Kaynig, Verena
AU - Fuchs, Thomas J.
AU - Buhmann, Joachim M.
C1 - Los Alamitos, CA, USA
C3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DA - 2010///
PY - 2010
DO - http://doi.ieeecomputersociety.org/10.1109/CVPR.2010.5540029
VL - 0
SP - 2902
EP - 2909
PB - IEEE Computer Society
SN - 978-1-4244-6984-0
UR - http://www.computer.org/portal/web/csdl/doi/10.1109/CVPR.2010.5540029
ER -
Geometrical Consistent 3D Tracing of Neuronal Processes in ssTEM Data.
Verena Kaynig, Thomas J. Fuchs and Joachim M. Buhmann.
Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II, p. 209–216, MICCAI'10, Springer-Verlag, Berlin, Heidelberg, ISBN 3-642-15744-0 978-3-642-15744-8, 2010
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@inproceedings{kaynig_geometrical_2010,
    address = {Beijing, China},
    series = {{MICCAI}'10},
    title = {Geometrical {Consistent} 3D {Tracing} of {Neuronal} {Processes} in {ssTEM} {Data}},
    isbn = {3-642-15744-0 978-3-642-15744-8},
    url = {http://portal.acm.org/citation.cfm?id=1928047.1928075},
    booktitle = {Proceedings of the 13th international conference on {Medical} image computing and computer-assisted intervention: {Part} {II}},
    publisher = {Springer-Verlag, Berlin, Heidelberg},
    author = {Kaynig, Verena and Fuchs, Thomas J. and Buhmann, Joachim M.},
    year = {2010},
    pages = {209--216}
}
Download Endnote/RIS citation
TY - CONF
TI - Geometrical Consistent 3D Tracing of Neuronal Processes in ssTEM Data
AU - Kaynig, Verena
AU - Fuchs, Thomas J.
AU - Buhmann, Joachim M.
T3 - MICCAI'10
C1 - Beijing, China
C3 - Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
DA - 2010///
PY - 2010
SP - 209
EP - 216
PB - Springer-Verlag, Berlin, Heidelberg
SN - 3-642-15744-0 978-3-642-15744-8
UR - http://portal.acm.org/citation.cfm?id=1928047.1928075
ER -
Related Topics:
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