cells

Human colon tissue

Submitted by Perrine on Mon, 08/26/2019 - 11:30

One of the principal challenges in counting or segmenting cells or cell nuclei is dealing with clustered objects such as in tissues. To help assess algorithms' performance in this regard, synthetic 3D image sets of human colon tissue are provided in two diferent levels of quality: high SNR and low SNR. Ground truth is available as well.

Description

The interactive Watershed Fiji plugin provides an interactive way to explore local maxima and threshold values while a resulting label map is updated on the fly.

After the user has found a reliable parameter configuration, it is possible to apply the same parameters to other images in a headless mode, for example via ImageJ macro scripting.

Description

The workflow computes cell-based colocalisation of two stainings in 2-D images. Both pixel- and object-based readouts are provided and some pros and cons are discussed. Please read here for more information:

https://github.com/tischi/ImageAnalysisWorkflows/blob/master/CellProfil…

 

Input data type: 

images

Output data type: 

processed images, numbers, text file, csv files

Description

Image segmentation based on the MOSAIC Discrete region competition algorithm.