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Network Analysis of Cell Communities

cytoNet is a cloud-based image analysis software designed to quantify the structure of cell communities from microscope images, using principles of graph theory.


HUVEC images
Example image (left) processed by cytoNet (right). cytoNet connects adjacent endothelial cells by edges (yellow) to quantify community structures, while characterizing the cells' morphology and protein expression. Mask generated by custom algorithm. Blue = nuclei. Red = microtubules. Green = actin.

NPC images
Nuclei of neural stem cells (left), and the corresponding network representation obtained through cytoNet (right). Default segmentation applied to obtain masks.

Step 1: Getting Started

To process your microscope images, upload them and follow Step 2 below, as described in our user guide. For more information, please see our archived manuscript.

Select image files:

max. # files = 50; max. total file size = 100 MB

No images but want to try out cytoNet?

Choose one or both of the following sample images (click on link to see high resolution masks):



and follow Step 2 below.
Use nucleus segmentation algorithm (default)
Use calcium signalling segmentation and analysis (requires stacked .tif files)
How to connect cells?
Centroid-distance method
Border overlap method
Select scaling factor.
The higher the scaling factor, the more edges in the graph. We recommend starting with the minimum scaling factor.
Select layer number:
For file formats that allow multiple images per file (e.g. stacked TIFF files), select the number of the layer you would like to be used for processing. To process multiple layers, enter numbers separated by commas (e.g. 1, 3). To process a range of layers, enter numbers separated by a dash (e.g. 1-3). The default layer number is 1 if none is specified.
Email address:
This email address will be used to notify you that processing is complete.
Re-enter email address:
Enter Request ID