Ústav molekulární genetiky AV ČR, v. v. i.
The course concentrates on image data segmentation and cell tracking through the application of cutting-edge deep learning methods, with a specific emphasis on their practical applications.
This hands-on course focuses on image data processing, segmentation and cell tracking using well-established and practically validated deep learning tools, including StarDist, Cellpose, Omnipose, MitoSegNet, and Noise2Void. We will demonstrate how segmentation enables quantitative image analysis and how tracking extends this to dynamic cell studies over time (TrackMate in Fiji, Delta2 framework). The course also introduces ZeroCostDL4Mic and includes a sponsored lecture on Zeiss arivis Cloud, covering cloud-based image processing and deep learning applications. A VR-based practical session will conclude the program.
The final day is dedicated to independent practical exercises based on the topics covered, allowing participants to consolidate and apply their knowledge.
This course is loosely related to Processing and Analysis of Microscopic Images in Biomedicine (PAMIB). Participants are expected to have an intermediate understanding of image processing and be comfortable working in Fiji. Programming skills for Python that will be used in the course are not required–basic interaction with scripts (e.g., editing file paths or filenames) is sufficient.
The course is conceived as an introduction to modern AI-based image analysis methods–an “unlocking” course–while reinforcing essential foundations (Fiji workflow, core image processing concepts).
