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, and 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 following 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 decision support.

As a partner in the A6-WP12 work package (Decision Support Systems) the FAU Erlangen-Nuremberg contributes to bridging the gap between signal data received from MR acquisition devices, i.e. (pediatric) brain scans, and a semantic understanding of the depicted entities. Once this is achieved it will be possible to combine automatically generated semantic descriptions of the imaging data with other sources of case specific information.

Brain Tissue Segmentation

Pediatric Brain Tumor Segmentation

Deep Gray Matter Segmentation