The datasets here are used to test 3DeeCellTracker v1.0, a deep-learning based software to track cells in 3D time lapse images.
Data description:
stardist_models:
stardist_worm1: A 3D StarDist model trained with worm1 data (t=1)
stardist_worm4: A 3D StarDist model trained with worm4 data (t=1)
ffn_models:
ffn_worm3: An FFN model trained with worm3 data (t=1)
ffn_worm4: An FFN model trained with worm4 data (t=1)
worm1_stardist_training_data:
Data for training 3D StarDist
worm1
data: The 3D time lapse image of a semi-immobilized worm.
manual_vol: The manually corrected segmentation in volume 1
worm4
data: The 3D time lapse image of a "straightened" freely moving worm.
manual_vol: The manually corrected segmentation in volume 1
Links for further information:
1. GitHub repository of [3DeeCellTracker][1]
2. The eLife paper ([Wen el al. 2021][2]) describing the algorithm, the data and the tracking results
3. The worm3 data comes from a previous study ([Toyoshima et al. PLoS Comput. Biol. 2016][3])
3. The files under "worm4/data" are a part of [another dataset][4] in a previous study ([Nguyen et al. PLoS Comput. Biol. 2017][5])
[1]: https://github.com/WenChentao/3DeeCellTracker
[2]: https://elifesciences.org/articles/59187
[3]: https://doi.org/10.1371/journal.pcbi.1004970
[4]: https://ieee-dataport.org/open-access/tracking-neurons-moving-and-deforming-brain-dataset
[5]: https://doi.org/10.1371/journal.pcbi.1005517
