Imperial College of Science, Technology and Medicine  

ICL  United Kingdom
  Rachel Groom, Sandie Bernor Room 1013 Blackett Laboratory ICL  
Principal Investigator(s):
  Dr. Daniel Rückert

List of researchers and collaborators involved in the project:

  Dr. Daniel Rückert
Dr. Alexander Schmidt-Richberg
Dr. Robin Wolz
  WP2 Novel imaging disease biomarkers & methods for model personalization
Main competencies:
Imperial College London is consistently rated among the world's top university institutions. It is ranked 8th in the world according to The Times Higher Education World University Rankings 2011-2012. The Department of Computing is one of the largest computing departments in the UK and is a world leader in academic research in computer science. There are over sixty academic staff actively involved in research from distributed computing, logic and artificial intelligence, high-performance computing, visual information processing, computing theory, to computational aspects of management science. The Biomedical Image Analysis (BioMedIA, ICL) Group (, The mission of the group is to develop novel, computational techniques for the analysis of biomedical images. It focuses on both translational and cutting edge research in methodology driven areas of biomedical image analysis, such as registration, segmentation and shape modelling, addressing real-world problems in healthcare. Current work includes applications in machine learning, computer aided diagnosis, computer assisted therapy and interventions as well as clinical applications of medical image computing in neurology and oncology. The group has three academic staff, over 30 research staff and students, and has attracted significant funding from research councils, industry and charities.

Contributions to the project
The BioMedIA group has significant expertise in the area of medical image computing, in particular in neuro-, cardiac and cancer image analysis. The BioMedIA group also led the work package on the quantification of imaging data in the PredictAD project. This has included the development of new methods for image segmentation and spatial normalisation. In addition, the VIP group has an active role in developing approaches for building statistical models from biomarker data in the BioMedIA project.