Research project

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

Wels, Michael ; Staatz, Gundula ; Rossi, Andrea ; Huber, Martin ; : 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 ; : A Boosting Approach for Multiple Sclerosis Lesion Segmentation in Multi-Spectral 3D MRI. In: 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 ; : 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 ; ; 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 ; : 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 ; ; 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 ; ; 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.