Python for Bioimage Analysis
Graduate School of Health and Medical Sciences at University of Copenhagen
This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH.
Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrolment deadline, available seats will be allocated to the waiting list.
The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at NorDoc member faculties. All other participants must pay the course fee.
Learning objectives
A student who has met the objectives of the course will be able to:
1. Write and run simple Python scripts to load, manipulate and automate the analysis of microscopy images, using constructs such as variables, conditionals, loops and functions.
2. Visualise, annotate and explore multidimensional image data in the napari viewer, and control napari programmatically from Python and Jupyter notebooks.
3. Understand the principles of digital image formation and classical image processing workflows, including filtering, thresholding and object segmentation, extraction of quantitative measurements (e.g. count, intensity, size and morphology).
4. Apply machine and deep learning methods for bioimage analysis, including segmentation models such as Random Forest classifiers and Cellpose, and judge when such methods are appropriate.
5. Combine these skills into creating reproducible end-to-end image analysis workflow that can be applied appropriately to their own image data.
Content
Python scripting provides a general, flexible and freely available toolset for researchers to quantify and analyse the digital images acquired from microscopy and other imaging modalities. It enables researchers to automate image processing and to connect their image analysis with downstream steps such as statistical testing and data visualisation, producing results that are quantitative, transparent and reproducible.
The course begins with an introduction to basic Python programming, using image data to familiarise students with core programming concepts (e.g. variables, conditionals, loops and functions) in a research-relevant manner, so that no prior programming experience is required. Building on this foundation, students gain insight into what a digital image is, what its pixel values represent, and how common image processing operations such as filtering and thresholding work and when to apply them, using popular Python libraries including scikit-image and matplotlib. Students then learn to segment images to identify objects of interest in their data and to measure features such as quantity, intensity, size and morphology.
On the second day, the course moves to machine learning and deep learning for bioimage analysis. Students are introduced to napari, a modern interactive image viewer, which they will use both to visualise and annotate multidimensional data and to access programmatically from Python and Jupyter notebooks. Students also learn how classification and segmentation methods work, how to apply and train deep learning models such as U-Net and Cellpose, and how to judge when such approaches are appropriate for a given research question.
Finally, on the last day, they then combine the skills from across the course into an end-to-end analysis workflow built with napari and Jupyter. Throughout, emphasis is placed on building analysis pipelines that are reproducible and appropriate to the research question at hand. The course concludes with dedicated time for participants to apply their newly acquired skills to their own image data under the supervision of the teachers.
Participants
PhD students who use microscopy in their research projects, and who would like to learn image analysis methods for extracting quantitative information from their image data in an automated and reproducible way.
Relevance to graduate programmes
The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH:
All graduate programmes
Language
English
Form
Each day of this 3-day course consists of lectures followed by hands-on, in-class practical exercises carried out together with discussions facilitated by the teachers. The final session provides dedicated time for students to apply the methods to their own image data with support from the teachers. Students will be given access to a pre-configured cloud-based teaching environment based on Azure and are expected to bring their own laptop; any software or setup instructions will be sent ahead of the course.
Course director
Clara Prats, Assoc. Prof., University of Copenhagen, cprats@sund.ku.dk
Teachers
Tricia Loo, PhD, University of Copenhagen
Julia Katharina Mertesdorf, MSc, University of Copenhagen
Jesko Wagner, PhD, University of Copenhagen
Dates
24 – 26 February 2027
Course location
University of Copenhagen, Panum Institute
Registration
Please register before 25 January 2027
Expected frequency
N/A
Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrolment.