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 one-day course focuses intensively on image data segmentation and cell tracking using state-of-the-art deep learning methods like StarDist, Cellpose, Omnipose, and MitoSegNet. It demonstrates how segmentation aids in analyzing image-based objects and how tracking applies segmentation to study cells dynamically over time. TrackMate, a plugin in Fiji, integrates StarDist and Cellpose for cell segmentation and tracking. The course also covers the Delta2 framework for tracking dense bacteria populations and explores ZeroCostDL4Mic, a cloud computing framework with advanced deep learning methods for microscopy tasks like segmentation, object detection, and denoising. The course aims to highlight the practicality and user-friendly nature of these deep learning techniques.
Also, a sponsor’s lecture on Apeer.com, Zeiss’s cloud platform, will focus on image processing, including machine and deep learning. At the course’s end, participants can actively practice segmentation and cell measurement using a virtual reality system from the same company.