Workflow

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.

An imageJ/Fiji plugin that measures and classifies neurites from a very large number of neurons.

A plugin for the ImageJ platform that automates measurement of retinal nuclear layer thickness (e.g., outer nuclear layer (ONL), inner nuclear layer (INL)) by placing callipers perpendicular to the contour of segmented layers, enabling rapid, reproducible quantification across large images and multiple modalities.

BraiAn is an open-source suite of tools designed to simplify signal quantification, analysis and visualization of large datasets typically obtained in whole-brain imaging experiments, following registration to an atlas. 

The package consists of two separate modules.

1. BrainAnDetect: A QuPath extension for multi-channel cell segmentation across large and variable datasets. It leverages QuPath's built in algorithms for cell detection, and features additional options for refining signal quantification, including machine-learning-based object classification, region-specific cell segmentation, multiple marker co-expression analysis, and an interface for selective exclusion of damaged tissue portions.
2. BraiAnalyse: A modular Python library for the easy navigation, visualization, and analysis of whole-brain quantification outputs.

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)