BioImage InformaticsNational facility
The BioImage Informatics Facility provides support and education in image analysis. We develop computational methods that automatically analyze and understand images of biological processes, using microscopic images as their primary source of information. We draw upon methods from computer vision, machine learning, statistics, and bioinformatics to quantify image data and answer biological questions. We do not primarily analyze data for our users, but rather help users get started with their own analysis. The SciLifeLab BioImage Informatics Facility has two nodes; one in Stockholm, connected to the School of Computer Science and Communication at KTH, and one in Uppsala, at the Centre for Image Analysis, Dept. of Information Technology, Uppsala University.
- Advice on best-practice and guidance on overall experimental design (staining, sample preparation, and image acquisition) for research involving microscopy imaging and quantitative data analysis.
- Guidance on image analysis assay development, including image processing algorithm development and software engineering to address challenging project goals.
- Advice on best-practice and guidance on high throughput/large-scale image processing using computing clusters, including data transfer and storage during the activity of the project.
- Guidance on large-scale data analysis and visualization
Courses and workshops
Part of our research support is provided in the form of courses, workshops and invited talks at courses related to digital image processing and analysis in life science.
Upcoming courses where we are involved:
- Digital image analysis for scientific applications Uppsala, October-December, 2018. This course aims at giving doctoral students from different disciplines sufficient understanding to solve basic computerized image analysis problems. The course will also offer an introduction to a number of freely available software tools (CellProfiler, ImageJ and ilastik), preparing the students to start using computerized image analysis in their own research. For more information, see http://www.cb.uu.se/~robin/DIASA2018/