Tracking Moving Cells in 3D Time Lapse Images Using 3DeeCellTracker

Recent advancements in 3D microscopy have enabled scientists to monitor signals of multiple cells in various animals/organs. However, segmenting and tracking the moving cells in three-dimensional time-lapse images (3D + T images), to extract their dynamic positions and activities, remains a considerable bottleneck in the field. We developed a deep learning-based software pipeline called 3DeeCellTracker, which precisely tracks cells with large movements in 3D + T images, obtained from different animals or organs, using highly divergent optical systems. In this protocol, we explain how to set up the computational environment, the required data, and the procedures to segment and track cells with 3DeeCellTracker. Our protocol will help scientists to analyze cell activities/movements in 3D + T image datasets that have been difficult to analyze. Graphic abstract: The flowchart illustrating how to use 3DeeCellTracker. See the Equipment and Procedure sections for detailed explanations.