Workshop material

Cell tracking is a common bio-image analysis task. In this session we will learn about the basic principles behind cell tracking. We will go through cell segmentation, spot detection techniques such as Difference of Gaussian, linking, matching and will see how to do cell tracking in practice using TrackMate in Fiji.

• Interact programmatically with the BioImage Archive and other data resources
• Apply pre-built machine learning models to image data
• Train and retrain machine learning models on image data
• Utilise machine learning approaches for object detection, image segmentation and de-noising
• Generate quantitative conclusions from images

Thie lecture is for Python beginners who want to dive into image processing with Python. It specifically aims for students and scientists working with microscopy images in the life sciences. We start with python basics, dive into descriptive statistics for working with measurements and matplotlib for plotting results.

These slides give an introduction to user interfacre customization in ImageJ using ImageJ Macro and to ImageJ Macro Markdown.

Big thanks to Jerome Mutterer (IBMP) and Nicolas De Francesco (IMBICE) who shared material openly I reused when making these slides.

Supplementary material is available as well under this doi: 10.5281/zenodo.4561714

Talk about sharing and licensing materials such as [raw] data, manuscripts, code, slides, ... at the EMBO Course on Advanced Methods for Bio-image Analysis 2021 virtually in Heidelberg