Since the time of Robert Hooke, images observed through microscopes have played a central role in biological investigations. The integration of microscopy with computational science in recent years has dramatically increased its value: images are now considered as multidimensional numerical data, which can be processed and analysed quantitatively using computers. For treating images as measured values, knowledge on the nature of digital images, processing protocols and algorithms is required. Skills for using software tools should be accompanied with knowledge of what they are doing. However, methods for extracting quantitative data from micrographs has often been missing in the education of biologists.
This course will focus on computational methods for analysing images of single molecules, cells and tissues, to boost the learning process of participants who have an immediate need to deploy image analysis in their own research. For this, the course extends from basic principles to the actual implementation of workflows using scripting. By increasing their image analysis literacy, participants will greatly enhance the scope, creativity and achievements of their research projects. Expert knowledge will be gathered to create a world-leading course for image analysis in the field of biology.
Graduate students, Post-docs and Faculties, those who need to analyze their own image data. We expect at least some experience in handling image data, not a complete beginner. Very basic programming knowledge might help.
- ImageJ Basics
- ImageJ Writing Macros
- Matlab Basics & Image Analysis
- Colocalization Analysis
- 2D tracking and 3D tracking using - Trackmate and results interpretation using Mamut
- Directionality analysis of EB1 movement along microtubules.
- 3-D Tubular network analysis of blood vessels
- Quantitative evaluation of multi-cellular movements in Drosophila embryo
- Resolving the process of clathrin mediated endocytosis using correlative light electron microscopy
- Knowledge on the capability of bioimage analysis.
- More freedom in analyzing images using ImageJ and Matlab.
- Proficiency in scripting image analysis workflows.
- A stronger connection to the bioimage analyst community.
- Getting to know various biological problems and interests through other students.