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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.
This course is aimed at early-career scientists as well as staff scientists working in microscopy or image analysis facilities who already have experience in bioimage analysis. Moreover, participants should already provide or show the clear intent of providing bioimage analysis support to other researchers with less or no experience in bioimage analysis.
In selecting participants we look for scientific excellence, immediate application of the methods learned, motivation to disseminate and networking skills. We expect a solid background in bioimage analysis and at least basic programming skills.
- Microscopy image restoration
- Microscopy image segmentation
- Deep and shallow learning
- Image data and results visualisation
- Cell phenotyping
- Spatial statistics
- Train the trainer: how to teach image analysis
After this course, participants should be able to:
- Immediately apply what they have learnt to image data of their own
- Handle large microscopy images, e.g. whole slide scans and 3D data, with publicly available state-of-the-art tools
- Manage complex, multi-step image analysis projects for image quality control, noise reduction, and advanced data visualization
- Understand, train, and apply neural-networks for denoising, segmentation, and classification
- Diagnose and solve many “co-localisation” problems through a better grasp of the underlying spatial statistics
- Confidently operate a representative selection of image analysis software, including Fiji, QuPath, ilastik, morphographX, KNIME, Imaris, and APEER
- Do all of the above in a reproducible and shareable manner