Fiches de cours

Image Processing for Life Science


Lecturer(s) :

Burri Olivier
Guiet Romain
Seitz Arne




Every year


This course is open to max. 16 students. To register, contact EDMS program administrator


Registration details will be announced via email. It takes place from September to December & intends to teach image processing with a strong emphasis of applications in life sciences. The idea is to enable the participants to solve image processing questions via workflows independently.


Over the last decades, the images arising from microscopes in Life Sciences went from being a qualitative support of scientific evidence to a quantitative resource.
To obtain good quality data from digital images, be it from a photograph of a Western blot, a TEM slice or a multi-channel confocal time-lapse stack, scientists must understand the underlying processes leading to the extracted information. Of similar importance is the software used to obtain the data.


It is is open to max. 16 students selected by the organizer. This course makes use of the ImageJ (FIJI package) as well as other open-source tools to ensure maximum reproducibility and protocol transfer of the analysis pipelines. The course will span 14 weeks with 1h30 of lecture per week, as well as exercises to complete outside of the course and will enable to students to establish image analysis workflows autonomously. The first seven weeks will be covered by a MOOC (self-paced) whereas the second part will be given as on-site course. Having successfully finished the MOOC is a prerequisite to participate in the second part.

This 14-week course aims to introduce students to digital image analysis in the context of life sciences. We will cover the following topics:


- Digital image data representations, formats, metadata
- Image manipulation
- Macro and script creation
- Filtering, linear, non-linear, morphological
- Segmentation
- Regions of interest
- Image stitching
- Image visualisation
- Data extraction and representation
- Image deconvolution and denoising
- Machine learning

Each topic will have a strong emphasis on good practices and will be followed by exercises to be handed out at the next session. Exercises will involve the creation of macros or scripts to reach a defined goal. The exercises are to be completed as autonomous homework, outside of lecture hours.


Biology, Image Processing, Microscopy, ImageJ, FIJI, Macros, Data, Segmentation,
Filtering Visualisation Open so

Assessment methods





In the programs

Reference week

      Exercise, TP
      Project, other


  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German