Cellular and Molecular Image Analysis

One of the main challenges of biomedical research in the postgenomic era is the unraveling of the molecular mechanisms of life. This is facilitated by recent advances in molecular probing and imaging technologies, which are having an enormous impact on the basic life sciences as well as human health care, by enabling better visualization of disease processes, the development of new biomarkers for early diagnosis, and enhanced preclinical validation of novel treatments in small-animal models as a first step towards clinical implementation. Studies into dynamic phenomena at the cellular and molecular levels generate vast amounts of spatiotemporal image data, containing much more relevant information than can be analyzed by human observers. Hence there is a growing need for automated quantitative analysis of time-lapse imaging data, not only to cope with the rising rate at which images are acquired, but also to reach a higher level of sensitivity, accuracy, objectivity, and reproducibility.

The goal of this theme group is to develop advanced image processing and analysis methods to enable efficient, accurate, and reproducible quantification and characterization of cellular and molecular dynamic processes. This is accomplished by 1) developing advanced methods for image restoration, enhancement, and super-resolution, 2) developing model-based methods for image segmentation, image registration, object detection, tracking, and motion analysis, 3) making efficient and robust implementations of developed methods in the form of software tools, 4) carrying out thorough evaluations of the methods by means of computer simulations as well as by expert human judgments, and 5) assessing the practical value of the methods for molecular imaging studies by using them to answer biologically and clinically relevant research questions, in collaboration with other groups.


Associated People

Research Staff


PhD Students


  • Gadea Mata