- Short Introduction to Fiji / ImageJ and basic functions
- Overview over proper scientific image file formats, metadata, bit depth
- In-depth practical introduction to pre-processing
- Automatic uneven lighting correction
- Understanding of image filters and their use cases for improved object detection
- Automatic background subtraction algorithms to reduce unspecific signal
- Object segmentation with automatic intensity thresholds (semantic segmentation)
- Individual object labeling (instance segmentation)
- Basic insight into some user friendly machine learning plugins in comparison to classical segmentation
- Optimization of segmentation with post-processing methods
- Quality control of segmentation results
- Different basic automatic analyses (object counting, measurements, shapes, intensities)
- Optional dependent on time: How to deal with 3D data in image analysis
- Short introduction to macro recording for automation