Cell tracking




DeepCell is neural network library for single cell analysis, written in Python and built using TensorFlow and Keras.

DeepCell aids in biological analysis by automatically segmenting and classifying cells in optical microscopy images. This framework consumes raw images and provides uniquely annotated files as an output.

The jupyter session in the read docs are broken, but the one from the GitHub are functional (see usage example )




SIMPLETRACKER a simple particle tracking algorithm that can deal with gaps.

Tracking , or particle linking, consist in re-building the trajectories of one or several particles as they move along time. Their position is reported at each frame, but their identity is yet unknown: we do not know what particle in one frame corresponding to a particle in the previous frame. Tracking algorithms aim at providing a solution for this problem. 

simpletracker.m is - as the name says - a simple implementation of a tracking algorithm, that can deal with gaps. A gap happens when one particle that was detected in one frame is not detected in the subsequent one. If not dealt with, this generates a track break, or a gap, in the frame where the particle disappear, and a false new track in the frame where it re-appear. 

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MaMuT is an end user plugin that combines the BigDataViewer and TrackMate to provide an application that allow browsing, annotating and curating annotations for large image data.

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The purpose of the workflow is ....

First you need

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QuimP is software for tracking cellular shape changes and dynamic distributions of fluorescent reporters at the cell membrane. QuimP's unique selling point is the possibility to aggregate data from many cells in form of spatio-temporal maps of dynamic events, independently of cell size and shape. QuimP has been successfully applied to address a wide range of problems related to cell movement in many different cell types. 


In transmembrane signalling the cell membrane plays a fundamental role in localising intracellular signalling components to specific sites of action, for example to reorganise the actomyosin cortex during cell polarisation and locomotion. The localisation of different components can be directly or indirectly visualised using fluorescence microscopy, for high-throughput screening commonly in 2D. A quantitative understanding demands segmentation and tracking of whole cells and fluorescence signals associated with the moving cell boundary, for example those associated with actin polymerisation at the cell front of locomoting cells. As regards segmentation, a wide range of methods can be used (threshold based, region growing, active contours or level sets) to obtain closed cell contours, which then are used to sample fluorescence adjacent to the cell edge in a straightforward manner. The most critical step however is cell edge tracking, which links points on contours at time t to corresponding points at t+1. Optical flow methods have been employed, but usually fail to meet the requirement that total fluorescence must not change. QuimP uses a method (ECMM, electrostatic contour migration method (Tyson et al., 2010) which has been shown to outperform traditional level set methods. ECMM minimises the sum of path lengths connecting all pairs of points, equivalent to minimising the energy required for cell deformation. The original segmentation based on an active contour method and outline tracking algorithms have been described in (Dormann et al., 2002; Tyson et al., 2010; Tyson et al., 2014).


ImageJ Plugin LineageTracker


## 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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Cell segmentation in phase contrast images


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


Measure cell volume over time


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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