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Research:

As researchers, we develop algorithms, computational methods and software tools to quantify and mine the rich information present in (primarily) microscopy images, increasing the scientific value of experiments based on image data. We draw upon methods from computer vision, machine learning, statistics, and bioinformatics to quantify image data and answer questions related to life science. Our three largest, externally funded projects are
• the ERC funded TissUUmaps project, focusing on analysis and visualization of tissue and in situ sequencing data.
• the SSF funded HASTE project, focusing on hierarchical approaches for large-scale microscopy experiments. A brief description in Swedish can be found here.
• the SSF funded SysMic project, focusing on quantifying and understanding cell dynamics, and the processes that control motion.
Our past and ongoing research projects are best reflected by our publications.

Support:




We provide research support and education in digital image processing and analysis for life science applications via the BioImagInformatics (BIIF) facility of SciLifeLab. We do not primarily analyze data for our facility users, but rather help users get started with their own analysis. We find that projects work best when researchers are closely involved in the analysis of their own data. The SciLifeLab BioImage Informatics Facility is headed by Petter Ranefall, and has two nodes; one in Stockholm, connected to the School of Computer Science and Communication at KTH directed by Kevin Smith, and one in Uppsala, at the Dept. of Information Technology directed by Carolina Wählby, Uppsala University. The node in Uppsala also involves Anna Klemm focusing on research support and education, and Amin Allalou and Hanqing Zhang working with technology development for large-scale quantitative phenotypic screening of zebrafish.

Upcoming workshops and activities are announced on the BioImagInformatics (BIIF) facility page. Apart from our own algorithm development, we work closely with developers of free and open-source CellProfiler software, where the most important contribution is the WormToolbox. Our code has also been incorporated in KNIME.