3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images.

ABSTRACT: Despite recent improvements in microscope technologies, segmenting and tracking 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, 3DeeCellTracker, by integrating multiple existing and new techniques including deep learning for tracking. With only one volume of training data, one initial correction, and a few parameter changes, 3DeeCellTracker successfully segmented and tracked ~100 cells in both semi-immobilized and 'straightened' freely moving worm's brain, in a naturally beating zebrafish heart, and ~1000 cells in a 3D cultured tumor spheroid. While these datasets were imaged with highly divergent optical systems, our method tr

SUBMITTER: Wen C 

PROVIDER: S-EPMC8009680 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature