This one-week school provides a hands-on introduction to image processing and analysis, with an emphasis on biologically relevant examples.
Covered topics will encompass :
- Basics in Image Formation
- ImageJ, from everyday use to advanced applications
- State-of-the-art analysis procedures like e.g. co-localisation
- Machine Learning including Deep Learning
- Stitching and registration
- Data analysis using KNIME
Bioimage analysis has become a keystone of biological research: the deluge of data produced by increasingly advanced microscopes calls for experts able to guide life scientists in the methods and software to be used to produce quantitative knowledge from this data. Due to the complexity of the data, without such expert guidance, it is very likely that image analysis algorithms may be applied incorrectly, possibly even producing erroneous results. Moreover, the diversity of imaging modalities, analysis algorithms and software solutions is growing so rapidly that even experts are overwhelmed.
This advanced course concentrates on teaching cutting-edge concepts and tools for quantitative image analysis, and will seek to upgrade the competencies of future bioimage analysis experts on both theoretical algorithm advancements as well as on practical implementation skills.
The workshop will last 3 ½ days and is built on the basis of an alternation between theoretical and practical sessions. Both will be in English and provided by national expert trainers.
On day 2 a mini-symposium will be organized to present the last developments in different LSFM domains (instrumentation, sample preparation and data analysis) done by international experts. This mini-symposium will be open to a larger audience in order to promote LSFM approaches to the local research community.
The workshop will encompass various theoretical aspects of LSFM from sample preparation, to image acquisition and first post-processing steps (3D reconstruction, visualization).
The workshop will span various scales that can be imaged with LSFM based on at least 4 different LSFM set-ups.