Cell tracking

3DeeCellTracker is a deep-learning based pipeline for tracking cells in 3D time-lapse images of deforming/moving organs.

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.

Ultrack is a versatile and scalable cell tracking method designed to address the challenges of tracking cells across 2D, 3D, and multichannel timelapse recordings, especially in complex and crowded tissues where segmentation is often ambiguous. By evaluating multiple candidate segmentations and employing temporal consistency, Ultrack ensures robust performance under segmentation uncertainty. Ultrack's methodology is explained here.

(from https://github.com/royerlab/ultrack)

CellStich proposes a set of tools for 3D segmentation from 2D segmentation: it reassembles 2D labels obtained from cell in slices in unique 3D labels across slices. It isparticularly robust to anisotropy, and is the ideal companion to cellpose 2D models or other 2D deep learning based models. One could also think about using it for cell tracking by overlap (using time as a third dimension).

ELEPHANT is a platform for 3D cell tracking, based on incremental and interactive deep learning.
It implements a client-server architecture. The server is built as a web application that serves deep learning-based algorithms. The client application is implemented by extending Mastodon, providing a user interface for annotation, proofreading and visualization.

btrack is a Python library for multi object tracking, used to reconstruct trajectories in crowded fields. btrack implemented a residual U-Net model coupledd with a classification CNN to allow accurate instance segmentation of the cell nuclei. To track the cells over time and through cell divisions, btrack developed a Bayesian cell tracking methodology that uses input features from the images to enable the retrieval of multi-generational lineage information from a corpus of thousands of hours of live-cell imaging data.