RRC ID 71635
Author Wen C, Miura T, Voleti V, Yamaguchi K, Tsutsumi M, Yamamoto K, Otomo K, Fujie Y, Teramoto T, Ishihara T, Aoki K, Nemoto T, Hillman EM, Kimura KD.
Title 3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images.
Journal Elife
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 tracked 90-100% of the cells in most cases, which is comparable or superior to previous results. These results suggest that 3DeeCellTracker could pave the way for revealing dynamic cell activities in image datasets that have been difficult to analyze.
Volume 10
Published 2021-3-30
DOI 10.7554/eLife.59187
PII 59187
PMID 33781383
PMC PMC8009680
MeSH Animals Brain / diagnostic imaging Caenorhabditis elegans / cytology Cell Tracking / instrumentation Cell Tracking / methods* Deep Learning* Heart / diagnostic imaging Image Processing, Computer-Assisted / instrumentation Image Processing, Computer-Assisted / methods* Imaging, Three-Dimensional / instrumentation Imaging, Three-Dimensional / methods* Spheroids, Cellular Time-Lapse Imaging / instrumentation Time-Lapse Imaging / methods* Tumor Cells, Cultured Zebrafish
IF 7.08
Resource
Human and Animal Cells HeLa(RCB0007)