In modern basic life science research as well as in drug discovery, recording and analyzing the images of cells over time using 3D microscopy has become extremely important.
Once the images have been recorded, the same cell in different images at different time points has to be accurately identified ("cell tracking") because the living cells captured in the images are in motion. However, tracking many cells automatically in 3D microscope videos has been considerably difficult.
In the Kimura laboratory at the Nagoya City University, Dr. Chentao Wen and colleagues developed the 1st AI-based software called 3DeeCellTracker that can run on a desktop PC and automatically track cells in 3D microscope videos. Using the software, they were able to measure and analyze the activities of ~100 cells in the brain of a moving microscopic worm, in a naturally beating heart of a young small fish, and ~1000 cancer cells cultured in 3D under laboratory conditions, which were recorded with different types of cutting-edge microscope systems.
This versatile software can now be used across biology, medical research, and drug development to help monitor cell activities.
Wen, C., et al. (2021) 3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images. eLife. doi.org/10.7554/eLife.59187.