Bioimage analysis with Napari

Description: In this course, we will learn how to use the free open-source software (FOSS) Napari for bioimage analysis. Napari is a fast, interactive, multi-dimensional image viewer for Python. It's designed for browsing, annotating, and analyzing large multi-dimensional images. Being highly extensible, Napari can be used to perform state-of-the-art image analysis in a user-friendly environment. The course focuses on the practical application of Napari and requires no preliminary knowledge of Python.

Next course time: N­euronal Circuit Dysfunction of C­NS diseases (WPM31), June 30, 2023, 9:30 am to 4 pm, July 5 and 6, 2023, 1 pm to 4 pm
Venue: DZNE Bonn (room number see handouts)

duration: 12.5 h for lectures including hands-on sessions

published: June 25, 2023

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Current announcements

Nothing a the moment.

Course requirements

  • Please, bring a laptop.
  • Please, install conda before the course starts. Follow Step 1 in this installation guide.
  • Please, download the course material, the links are provided in the installation guide.
  • You can also bring your own images you have taken in the lab.


Tutorial 1: Napari installation guide

Tutorial 2: Basic handling of Napari

Tutorial 3: Reslicing stacks in Napari

Tutorial 4: Cropping images in Napari

Tutorial 5: Scale bars and adjusting image scaling

Tutorial 6: The Napari Assistant

Tutorial 7: Image denoising and background subtraction in Napari

Tutorial 8: Bleach correction in Napari

Tutorial 9: Spectral unmixing in Napari

Tutorial 10: Image registration in Napari

Tutorial 11: Image segmentation and feature extraction

Tutorial 12: Advanced cell segmentation with Cellpose

Tutorial 13: Segmenting densly packed cells with StarDist

Tutorial 14: Colocalizing cells

Tutorial 15: Tracking cell migration

Tutorial 16: Filament tracing

Tutorial 17: Data exploration, dimensionality reduction and clustering with Napari


Image credits


Unless otherwise stated, please perform the examples shown in each tutorial. Use the image file(s) shown in the example or any other file provided in GitHub data folder. You can also use your own images taken in the lab or an appropriate sample image from the Cell Image Library. Also try to combine the techniques you have already learned in the previous tutorials (if appropriate). Each tutorial including reading time and exercise takes around 15-30 minutes.

Past courses

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This course material is under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0).


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