Workshop material

Fractal is a framework to process high-content imaging data at scale and prepare it for interactive visualization. Fractal provides distributed workflows that convert TBs of image data into OME-Zarr files. The platform then processes the 3D image data by applying tasks like illumination correction, maximum intensity projection, 3D segmentation using cellpose and measurements using napari workflows. The pyramidal OME-Zarr files enable interactive visualization in the napari viewer.
These slides are from an early demo of Fractal in November 2022

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