Back to Projects List

# Write full project title here

## Key Investigators

- Xiang Chen (Memorial University of Newfoundland)
- Oscar Meruvia-Pastor (Memorial University of Newfoundland)
- Touati Benoukraf (Memorial University of Newfoundland)

# 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’.

## Objective

- 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

- Collaborate with Slicer community members during this Project Week.

## Updates and Next Steps

- Currently my extension has already implemented the calculation functionality and the current goal is to increase the calculation speed.
- 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.

**Before:**

**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:**

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

# Illustrations

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