Rudolf Mößbauer Tenure Track
Technical University of Munich
Image-Based Biomedical Modeling
Before joining the Department of Informatics at TUM as the first Rudolf Mößbauer Tenure Track Professor in 2013, Bjoern Menze was senior researcher and lecturer in the Computer Vision Lab at ETH Zurich (Gabor Szekely), Switzerland, and a lab member of the Asclepios Project at INRIA Sophia Antipolis, France (Nicholas Ayache). Before that, he worked as a postdoc in Boston, USA, in the Medical Vision group of the CSAIL at MIT (Polina Golland) and in the BWH Surgical Planning Lab of Harvard Medical School (Ron Kikinis), after having been with the Department of Anthropology of Harvard University (Jason Ur). Bjoern Menze received his Ph.D. in Computer Science in 2007, while working at the Interdisciplinary Center for Scientific Computing in Heidelberg (Fred Hamprecht), Germany, and obtained degrees in Physics from Heidelberg and from Uppsala University, Sweden.
2014 MICCAI Young Scientist Award Finalist & Selection for the MICCAI
2014 Best Paper Issue of Medical Image Analysis. M Rempfer et al., B Menze. ‚Extracting vascular networks under physiological constraints via integer programming’. MICCAI Conference 2014, Boston, USA
2014 Best Poster Award. J Lipkova, B Menze, P Koumoutsakos. 'Patient-specific modeling of glioma' Platform for Advanced Scientific Computing (PASC) Conference 2014, Zurich, Switzerland
2009 Leopoldina Research Fellow of the German National Academy of Sciences Leopoldina 2008 DFG Research Fellowship 2007 Fritz Thyssen Research Fellowship 1998 Fellow of the German National Academic Foundation / Studienstiftung des deutschen Volkes
My research is in medical image computing, exploring topics at the interface of medical computer vision, image-based modeling and computational physiology. In this, I focus on applications in clinical neuroimaging and the modeling of tumor growth. My work strives towards transforming the descriptive interpretation of biomedical images into a model-driven analysis that infers properties of the underlying physiological and patho-physiological processes by using models from biophysics and computational physiology. I am also interested in how to apply such models to big data bases in order to learn about correlations between model features and disease patterns at a population scale.
- Mapping patterns of long-term settlement in Northern Mesopotamia at a large scale. Proceedings of the National Academy of Sciences 109 (14), 2012, E778-E787.
- Using spatial prior knowledge in the spectral fitting of MRS images. NMR in Biomedicine 25 (1), 2011, 1-13.
- A Generative Approach for Image-Based Modeling of Tumor Growth. In: Lecture Notes in Computer Science. Springer Science + Business Media, 2011.
- On Oblique Random Forests. In: Machine Learning and Knowledge Discovery in Databases. Springer Science + Business Media, 2011.
- A Generative Model for Brain Tumor Segmentation in Multi-Modal Images. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. Springer Science + Business Media, 2010.