Which plugins are for cell segmentation and volume measurement in 3D

Hello, we are working on a plugin for measuring the volume of cells in 3D. We are at a point now where we would like to compare our results to some of the different techniques that currently exist in fiji/imagej.

Our input are fluorescent labeled cells imaged with confocal microscopy. The cells are somewhat isolated and we are not trying to track them over time.

We are looking for plugins / or workflow, for segmentation and volume measurement. We are also considering more ways to save the results, the segmented cells.

Thank you

There is a great 3D ImageJ Suite (multiple plugins) available which you can find here or as a FIJI plugin:


With the 3D Analysis plugin you can perform several geometrical measurements:



My suggested procedure is:

  • First, make your image binary by thresholding (you can use the usual thresholding procedure; note that you are likely to want to use the stack histogram, and not recalculate the threshold per image). Your goal in this first thresholding step is not to get perfect segmentations, but to create an image where each cell is represented by only a single object. This means that your output may be very tiny pieces of each cell, but with each cell only having 1 such piece.
  • As @Bio7 points out, there is the 3D ImageJ Suite, specifically see http://imagejdocu.tudor.lu/doku.php?id=plugin:segmentation:3d_spots_segmentation:start and the 3D watershed plugin
  • You can use your thresholded cells as the watershed seed image, because you note that they are sparse/isolated, this is a critical assumption. Apply the watershed 3D to your original input image (the non-thresholded one), using the thresholded seed image. The watershed algorithm will grow from each seed based upon the intensity of the input image, or until it collides with the region being grown from an alternative seed. Each of these regions will be treated as individual objects, which is why it was important when creating the thresholded-seed image in the first step such that each cell is only represented by a single contiguous segment.
  • This should give you a new image where cells are unified 3D objects.
  • Then use the 3D Objects Counter, which will create a results table containing statistics about each object. You can then save this to Excel format.

If you have issues creating your initial seed image with thresholding alone, then you may want to also apply some binary image processing operations, such as erode, dilate, despeckle, etc… These can help you eliminate spurious segments that come from the same cell (remember, when creating your seed image for watershed-ing you only want 1 seed object per cell).

This is a related post, but the above procedure restates all of the relevant steps that you need: Volumic particles density

If the 3D object counter is too slow, see this post which basically says that BoneJ has a fast multithreaded 3D particle analyzer.



Hello @odinsbane,

To add more suggestions, I recommend you to have a look at the Morphological Segmentation plugin and the MorphoLibJ tools for geometrical and intensity measures.

It would also help if you upload an example of the images you’re trying to segment, so other users and developers can better understand your problem.



Great suggestions. We will take a look at these this week. @iarganda I will get an example up, it could take a bit of time. What is a good way to upload a tiff? Put it in a zip file?

TIFF or ZIP are fine. You can share them via a link to Dropbox o Google drive, for example.

I have a similar question about a useful ImageJ plugin for cell segmentation and measurements such as cell volume, cell location and intensity. Are there any new developments in the past year or so worth mentioning?
Is MorpholibJ, Morphological Segmentation and 3D ImageJ suite still the only options?
Thank you!

Hello @Vasya_Che and welcome to the ImageJ forum!

Yes, they are still good options. Have you given them a try?

We developed a plugin for fluorescent images. It work ok, especially for round roundish cells. It is pretty easy to setup and try if you’re already using fiji. We setup an update site, https://sites.imagej.net/Odinsbane/ that includes DeformingMesh3D plugin and JFilament. There is also a description on how to use it, http://www.ucl.ac.uk/lmcb/meshplugin

We’re working on version 0.34, which has a few improvements. If you have questions or requests feel free to ask.

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Hello Ignacio,
Thank you for your answer! I am new to these plugins so was curious which one would be best to focus on (maybe one that can handle a wider range of 3D image types). Since I just started it is not clear what kind of advantage is of one over another.
Kind regards,

That is great, thank you! I will take a look at this plugin as well.

If you can tell use a bit more about the images you’re using (or even better a sample image stack), somebody might be able to give you better information about what you should be looking at.


Here are a few example 3D stacks that I am most interested in: http://cbia.fi.muni.cz/projects/cytopacq-a-simulation-toolbox_6.html (e.g. HL60 and Granulocytes) to do segmentation and determine their characteristics I had mentioned.