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Health-e-Child The Health-e-Child Project is embedded in the European
Union's sixth framework program (FP6) that aims to
improve integration and coordination of research within
the European Union. Health-e-Child (project identifier:
IST-2004-027749) is scheduled from 01/01/2006 to
12/31/2009 with a total
funding of $ 12.2 million by the EC. The overall budget
is $ 16.7 million. The project's vision is the
development of an integrated healthcare platform for
European pediatrics that provides seamless integration of
traditional and emerging sources of biomedical
information. In the long run
Health-e-Child wants to provide access to biomedical
knowledge repositories for personalized and preventive
healthcare, large-scale information-based biomedical
research and training, informed policy making. For the
beginning the project focus will be on individualized
disease prevention, screening, early diagnosis, therapy
and follow-up of three representative pediatric diseases
selected from the three major categories: heart diseases,
inflammatory diseases, and brain tumors. By building a
Grid-enabled European network of leading clinical centers
it will be possible to share and annotate biomedical
data, validate systems clinically, and diffuse clinical
excellence across Europe by setting up new technologies,
clinical workflows, and standards. Health-e-Child's key
concept is the vertical and longitudinal integration of
information across all information layers of biomedical
abstraction (i.e., genetic, cell, tissue, organ,
individual and population layer) to provide a unified
view of a person's biomedical and clinical condition.
This will enable sophisticated knowledge discovery and
decisionsupport.
As a partner in the A6-WP12 work package (Decision
Support Systems) the FAU Erlangen-Nuremberg contributes
to the design, the development, and the training of a
classifier/predictor, which provides prediction of brain
tumor behaviors and treatment outcomes using clinical and
lab data, as well as neurophysiologic, neuropathologic,
neuroradiological, and genetic information. Through
cohesive exploitation of the diverse biomedical
information sources mentioned, the system will help to
enable innovative and
improved clinical practice. One goal that we work on for
the moment is the extraction of meaningful tumor features
from MR images in order to be used as input to the
classifier/predictor. One goal that project members at
FAU work on for the moment is the extraction of
meaningful tumor features from MR images that are
characteristic for the disease and can be used as input
to the DSS. In this context a proper and reliable
segmentation of the tumor tissue and the tumor's
different compartments is critical for further
processing steps.
| Project manager: Dr. Martin Huber, Prof. Dr.-Ing. Joachim Hornegger
Project participants: Dipl.-Inf. Dime Vitanovski, Dr.-Ing. Michael Wels
Keywords: Decision Support Systems, Data Integration, Segmentation
Duration: 1.1.2006 - 30.4.2010
Sponsored by: Siemens Corporate Technologies
Contact: Vitanovski, Dime E-Mail: dime.vitanovski@cs.fau.de
| Publications |
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Wels, Michael ; Staatz, Gundula ; Rossi, Andrea ; Huber, Martin ; Hornegger, Joachim: Anisotropic hidden Markov random field modeling for unsupervised MRI brain tissue segmentation and brain tumor detection. In: Lemke, Heinz U. ; Inamura, Kiyonari ; Doi, Kunio ; Vannier, Michael W. ; Farman, Allan G. (Ed.) : International Journal of Computer Assisted Radiology and Surgery Volume 2 Supplement 1 (Int J CARS (2007) (Suppl 1)) CARS 2007 Computer Assisted Radiology and Surgery Proceedings of the 21st International Congress and Exhibition (Computer Assisted Radiology and Surgery 21st International Congress and Exhibition Berlin, Germany 27.06.2007-30.06.2007). Vol. 2, 1. Edition Berlin : Springer Heidelberg, 2007, pp 457. | Wels, Michael ; Huber, Martin ; Hornegger, Joachim: A Boosting Approach for Multiple Sclerosis Lesion Segmentation in Multi-Spectral 3D MRI. In: Hornegger, Joachim ; Mayr, Ernst W. ; Schookin, Sergey ; Feußner, Hubertus ; Navab, Nassir ; Gulyaev, Yuri V. ; Höller, Kurt ; Ganzha, Victor (Ed.) : 3rd Russian-Bavarian Conference on Biomedical Engineering (3rd Russian-Bavarian Conference on Biomedical Engineering Erlangen 2.-3.07.2007). Vol. 1. Erlangen : Union aktuell, 2007, pp 116-120. - ISBN 3-921713-33-X | Wels, Michael ; Huber, Martin ; Hornegger, Joachim: Fully Automated Knowledge-Based Segmentation of the Caudate Nuclei in 3-D MRI. In: Heimann, Tobias ; Styner, Martin ; van Ginneken, Bram (Ed.) : 3D Segmentation in the Clinic - A Grand Challenge MICCAI 2007 Workshop Proceedings (10th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2007) Brisbane, QLD, Australien 29.10.2007 - 02.11.2007). 2007, pp 19-27. - ISBN 978-0-643-09523-6 | Wels, Michael ; Carneiro, Gustavo ; Brand, Michael ; Huber, Martin ; Hornegger, Joachim ; Comaniciu, Dorin: A Discriminative Model-Constrained Graph Cuts Approach to Fully Automated Pediatric Brain Tumor Segmentation in 3-D MRI. In: Metaxas, Dimitris ; Axel, Leon ; Fichtinger, Gabor ; Székely, Gábor (Ed.) : Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2008), Part I, LNCS 5241 (11th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2008) New York, NY, USA 06.09.2008 - 10.09.2008). Berlin : Springer, 2008, pp 67-75. (Lecture Notes on Computer Science 5241) - ISBN 3-540-44707-5 | Wels, Michael ; Huber, Martin ; Hornegger, Joachim: Fully Automated Segmentation of Multiple Sclerosis Lesions in Multispectral MRI. In: Zhuravlev, Yuri I. (Ed.) : Pattern Recognition and Image Analysis (OGRW 2007 Ettlingen 20.08.2007 - 23.08.2007). Vol. 18, 2. Edition 2008, pp 347-350. | Vitanovski, Dime ; Ionasec, Razvan ; Georgescu, Bogdan ; Huber, Martin ; Taylor, Andrew ; Hornegger, Joachim ; Comaniciu, Dorin: Personalized pulmonary trunk modeling for intervention planning and valve assessment estimated from CT data. In: Wang, Guang-Zhong (Ed.) : Proceedings of 12th Internation Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2009 London (UK) 20-24.9.2009). Heidelberg : Springer, 2009, pp 17-25. - ISBN 978-3-642-04267-6 | Voigt, Ingmar ; Vitanovski, Dime ; Ionasec, Razvan Ioan ; Tsymbal, Alexey ; Georgescu, Bogdan ; Zhou, Shaohua Kevin ; Huber, Martin ; Navab, Nassir ; Hornegger, Joachim ; Comaniciu, Dorin: Learning discriminative distance functions for valve retrieval and improved decision support in valvular heart disease. In: Haynor, David R. ; Dawant, Benoit M. (Ed.) : Proceedings of SPIE Medical Imaging 2010 (SPIE Medical Imaging 2010 San Diego, CA, USA 12.-17.02.2010). Bellingham, WA, USA : SPIE, 2010, pp no pagination. |
Institution: Chair of Computer Science 5 (Pattern Recognition)
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