Smart Microscopy - Image Analysis to
Improve Remote Image Acquisition

Centre for Cellular Imaging, University of Gothenburg in cooperation with Zeiss,

17th, 18th, 20th  and 21st of May 2021

Would you like to learn how to automate your microscopy workflows with automatic image analysis and intelligent feedback experiments?

In the “Smart Microscopy - Image analysis to improve remote image acquisition” Workshop you can learn from top researchers in the fields of Image Analysis, Smart Microscopy and Machine Learning, as well as the very creators of the ZEN and OAD (open application development) one of the most advanced platforms for microscopy automation and scripting. The workshop will be limited to 25 participants. It will take place remotely on the 17th, 18th, 20th and 21st of May (full days CET time). The participants and the trainers will together address current challenges in microscopy, image analysis and automation

Topics that will be covered on the hands on remote workshop:

• Image analysis in Python and ZEN

• Training your own Machine Learning model for segmentation in Python and ZEN

• Smart microscopy in ZEN: guided acquisition

• APEER modules based on Python

• Reading image data in Python and visualization in Napari

• Call for help event - round table discussions with the experts about your own microscopy and analysis challenges

Your trainers at the workshop:

Kristina Ulicna, University College London, UK, Developer of arboretum (Napari plugin)

Robert Haase, Technische Universität Dresden, Germany, Developer of CLIJ, clEsperanto, and others

Marion Lang, ZEISS, Product Manager OAD

Sebastian Rhode, ZEISS, Product Owner Machine Learning and Image Analysis

Rafael Camacho, University of Gothenburg, Sweden, Image analyst and Organizer

Julia Fernandez-Rodriguez, University of Gothenburg, Sweden, Head of the Centre for Cellular Imaging and Organizer

The requirements for the student are briefly as follows:

This is not a course to learn Python. We expect students to have intermediate scripting skills and knowledge in image analysis and microscopy. Students should understand the use of control statements (for-loops, if-statements) list data structures, and functions. We will give priority to students with a basic understanding of object oriented programming and useful Python packages such as numpy, skimage and scipy

Preliminary schedule: