Why take this course

Fiji and the ImageJ macro language have served the life sciences for many years very well in terms of bioimage data inspection and analysis. However, there are some downsides to this ecosystem, including that the ImageJ macro language is very specific to ImageJ and cannot be universally applied. In addition, the ImageJ macro language misses many features of standard programming languages and many new image analysis algorithms, especially deep-learning based, are usually first implemented in python, which is outside the Java-based Fiji eco-system.

In this course, you will be introduced to essential concepts of image analysis. You will learn how to use python for image analysis and take advantage of the large and growing number of specialized python libraries. Learning python is useful in general because it is currently the most popular language for scientific computing and data science. In addition, there is a well-established python-based image visualisation tool, namely napari, that allows powerful and flexible n-dimensional image data visualisation, including overlay of segmentation and annotation layers.

Prerequisites

Ideally, you should know all the topics mentioned in the Learn the Basics section of the Learn Python website (you may skip "Classes and Objects" and "Modules and Packages").
If you have time, it can be beneficial to practice running code in a Jupyter Notebook.

IT Setup

You will be guided on how to set up your python platform and how to install the necessary modules on day 1 of the course.

If you are already proficient with python and do not need to revisit the basics, you may skip the introductory and installation sessions. However, in this case please make sure to use the conda platform to install skimage and napari on your laptop before the course, following these instructions (Install skimage & napari).

Schedule and Location

The whole course will be guided hands-on sessions.

• Monday 05.10.2026 10:00 - 17:00, BioQuant SR042
• Tuesday 06.10.2026 10:00 - 17:00, BioQuant SR042
• Monday 12.10.2026 10:00 - 17:00, BioQuant SR043
• Tuesday 13.10.2026 10:00 - 17:00, BioQuant SR043

This includes a lunch break (1 hr) and two coffee breaks (20 minutes), resulting in approximately 5 hr of teaching time, covering 5-7 modules of different lengths.

Program

Please note that the exact modules and program are still subject to change.

Monday 05.10.2026: Python and image inspection basics (day 1)

The first half of the day is organized as an onboarding and refresh session to bring everyone to the same level on basic python programming. If you are already proficient with python, you may skip this part. In this case, please make sure to use the conda platform to install skimage and napari on your laptop before the course, following these instructions (Install skimage & napari).

1. Participants introduce themselves, where they work, why they joined the course, and what previous experience they have
2. Trainers introduce themselves, where they work and how they are competent. Any new trainers introduce themselves at the start of their modules
3. Introduction to python and basic environment management (Severina Klaus)
4. Tool installations (Severina Klaus)
5. Basics of python programming (Charlotte Kaplan)
6. Digital image basics (Severina Klaus)
7. Image data types (Severina Klaus)
8. Lookup tables (Severina Klaus)

Tuesday 06.10.2026: Image inspection and analysis basics (day 2)

1. Spatial image calibration (Julian Hennies)
2. N-dimensional images (Julian Hennies)
3. Image projections (Arif Khan)
4. Image neighborhood filtering (Arif Khan)
5. Statistical (rank) filtering (Severina Klaus)
6. Local background subtraction (Severina Klaus)

Monday 12.10.2026: Image analysis basics (day 3)

1. Segmentation (Severina Klaus)
2. Manual thresholding (Severina Klaus)
3. Automated thresholding (Severina Klaus)
4. Connected component labeling (Alessandro Ulivi)
5. Morphological filters (Alessandro Ulivi)
6. Object shape measurements (Charlotte Kaplan)
7. Object intensity measurements (Charlotte Kaplan)
8. Practical exercise - Workflow: Basic 2D object analysis

Tuesday 13.10.2026: Image analysis and batch processing (day 4)

1. Batch processing (Julian Hennies)
2. Loops (Julian Hennies)
3. Strings and path manipulation (Alessandro Ulivi)
4. Functions (Alessandro Ulivi)
5. Data inspection and visualisation (Charlotte Kaplan)
6. Outlook: Topics for advanced image analysis in Python (Victor-Didier Perez-Meza)
7. Practical exercise - Workflow: Batch processing
8. Summary of what was learned

Costs

The course is free of charge.