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Project Description

This is an extension for computing the percentage of colocalization(Spatial overlap between different channels) of Z-stack TIFF images, which developed for category ‘Quantification’.


  1. As of now, the computation of my module is a bit slow(when the threshold range for each selected channel is very large), so I’m hoping to get help from slicer experts to make it faster.

Approach and Plan

  1. Collaborate with Slicer community members during this Project Week.

Updates and Next Steps

  1. Currently my extension has already implemented the calculation functionality and the current goal is to increase the calculation speed.
  2. As shown below, The calculation time has been greatly reduced after removing all unnecessary code related to creating the closed surface representations for all segments.


Now (The calculation time has been shortened to less than 30s):

When the threshold range is set not that so large, the calculation time will be shorter:

Next Steps:

  1. Convert the volume corresponding to each channel in the ROI to a numpy array.
  2. Apply thresholding to all numpy array of the volumes within the ROI.
  3. Detect all intersections among all channels using numpy indexing.
  4. Count the number of voxels resulting from step 3 and multiply by the volume of one voxel.


Users can threshold the volume rendering of the input Z-stack image in the 3D view window, select the region of interest(ROI) by the bounding box, and get a Venn diagram that shows the critical metric of colocalization’s percentage. Extension ScreenShots

Background and References

The link to the source code repository

Download links to sample image