About the P2N Training School
The aim of this course is to lower the threshold for researchers who use microscopy or molecular imaging on a routine basis and/or demand image analysis for their own research by:
- Providing fundamental knowledge on the most important image properties, basic processing algorithms and typical analyses
- Giving direct hands-on training on how to perform very common tasks in open source software ranging from simple image annotation to more advanced deep learning applications.
An overview of different image analysis platforms and an introduction to basic scripting capabilities should further enable the participants to select or devise apt solutions for their image analysis needs. The primary goal is to familiarize PhD students, post-docs and lab technicians, who work with images of biological samples and have limited or no experience with the fundamentals of biological image analysis.
Learning outcomes
After this course participants should be able to:
- Know important image properties and processing algorithms
- Annotate and present images in a publication-ready format
- Learn how to handle large data files
- Know which analytical routine to select and apply
- Compose basic image analysis workflows
- Gain proficiency in basic scripting language
- Understand how deep learning can be applied to bioimage analysis
Organizing committee
The organizing committee is composed of the following members:
