3DeeCellTracker

The installation comprises a set of Jupyter notebooks and a library they depend on. The workflow steps include separate training and segmentation/tracking.

Examples of cell tracking from the reference publication are: ~100 cells in a freely moving nematode brain, ~100 cells in a beating zebrafish heart, and ~1000 cells in a 3D tumor spheroid.

has function

Cell segmentation

Cell tracking

has topic

Bioimage informatics

has biological terms

cell tracking

Post date

10/28/2025 - 02:26

Last modified

10/28/2025 - 03:00

Download Page

https://github.com/WenChentao/3DeeCellTracker

Documentation Link

Video tutorials

attribution instructions

Depending on the segmentation method you used (StarDist3D or U-Net3D), you may also cite either of following papers:
Martin Weigert, Uwe Schmidt, Robert Haase, Ko Sugawara, and Gene Myers. Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy. The IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass Village, Colorado, March 2020
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. MICCAI 2016. Lecture Notes in Computer Science, vol 9901. Springer, Cham.

Reference Publication Link

3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cel…

Usage Example Link

Github section containing Jupyter notebooks for training and segmentation

Backlinks