Cell tracking

Description

## Short Summary Quote from the plugin page: >LineageTracker offers an ImageJ based framework which is easily extendible and has the capability to track cell lineages while being specifically designed to handle large cell displacements between frames. The methods are designed for fluorescent cells and have been used to analyse Schizosaccharomyces pombe, C2C12 mouse stem cells or migrating RPE cells. This tool also allows flexible cell segmentation and extendable in all aspects. The webpage is detailed with usage from ImageJ macro. Rather than being simply a component, the plugin is indeed a framework with set of components. ## Misc info A tip from the plugin author in ImageJ mailing list (08.Sep.2015): > We have an additional script to export only a selected range of frames. I can send you that if you think LineageTracker is something for you. To be on the safe side I would try it with an older version of ImageJ. We have experienced some problems, mostly related to Java. Java 8 seems to fix most of it. ## References 2630: Application example. 2631: Plugin Paper.

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Description

Quote: *A GUI-based program which manually detects spots and places them into previously detected meshes. Currently the program runs from MATLAB only. *

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Description

This Matlab code demonstrates an edge-based active contour model as an application of the Distance Regularized Level Set Evolution (DRLSE) formulation.

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Description

The workflow measures the growth of cells in 3D, combining an ImageJ macro for preprocessing and successive tracking using Imaris.  

The sample dataset (available in the github repository) contains 2-Photon images of neurons. The neurons were imaged in 3D at two time frames.To allow measuring significant differences in cell volume, the time gap between the frames is large (ca. 30 min) and the animal was removed in the waiting phase. For this reason, there is a considerable shift in sample position between the frames that has to be corrected before cell detection and tracking.

The workflow consists of following steps:

1. Import of single tiff slices [imageJ macro]

2. Organizing the data in a 4D time series with 2 time frames [imageJ macro]

3. Correction of shift between the time frames by rigid registration [imagJ macro]

4. Bleaching correction [imageJ macro]

5. Export of preprocessed image data in ics/ids format [imageJ macro]

6. Import of ics/ids data to Imaris [Imaris]

7. Cell object detection as "Imaris Surface Object" [Imaris]

8. Tracking cell objects over time [Imaris]

9. Split Tracks (use Imaris XT extension "Split Tracks") to generate single cell objects [Imaris]

10. Export the statistics: Select the complete folder, go to the statistics tab and use ‚Full Export’ [Imaris]

The preprocessing macro can be referenced here: [![DOI](https://zenodo.org/badge/7683/cmohl2013/registration_and_bleaching_corr…)](http://dx.doi.org/10.5281/zenodo.13167)

The sample images were acquired by Cordula Ulbrich (Petzold Group at German Center of Neurodegenerative Disesases (DZNE), Bonn, Germany).

Input data type: tiff

Output data type: data table

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Description

CellX is an open-source software package of workflow template for cell segmentation, intensity quantification, and cell tracking on a variety of microscopy images with distinguishable cell boundary.

Installation and step-by-step usage details are described in Mayer et al (2013). 

After users provide a few annotations of cell sizes and cell boundary profiles, it tries to match boundary profile pattern on cells thus provide segmentation and further tracking. It works the best on cells without extreme shapes and with a rather homogeneous boundary pattern. It may not work well on images with cells of sizes only a few pixels. Its output comprises control images for visual validation, text files for post-processing statistics, and MATLAB objects for advanced subsequent analysis.