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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 quality control and image restoration
– Advanced image segmentation and complex cell phenotyping
– Handling large microscopy images (such as whole slide scans) and big N-dimensional data
– Neural-networks for image restoration, segmentation, and object classification.
– Co-localisation and spatial statistics
– Train the trainer: how to teach image analysis
Participants should be able to apply what they have learnt to their own image data as well as to image data of their colleagues.
For each module, participants will learn both theoretical and practical aspects of the latest developments in the fields of bio image analysis concepts and tools.
After this course, they are expected to be able to confidently operate and interface with a representative selection of open and closed source image analysis software, including Fiji, QuPath, ilastik, morphographX, KNIME, Imaris, and APEER, in a reproducible and shareable manner.
In addition, participants will gain new insights about how to set up an image analysis course in their own institution.
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