CIF Tutorial | Fiji | Segmenting Muscular Fibers

CIF Tutorial | Fiji | Segmenting Muscular Fibers
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[Summary]
How to use Fiji along with MorpholibJ and StarDist to segment muscular fibers from a color slide scanner image.
NB : No method is perfect and this one is no exception. It can obviously benefit from further refinements like better filtering, and maybe manual label edition to correct obvious mistakes. There are also other methods involving cellpose and python for this specific task, but it's more complicated to implement for the beginners. (see below).
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[Notions viewed]
- Fiji and Image type, plugin installation, label images, measurements, scales
- StarDist
- MorphoLibJ
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[Images]
Gastrocnemius muscle cross sections, courtesy of Martino Guiotto, Pietro Di SUMMA group.
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[Links]
Fiji: imagej.net/sof...
MorphoLibJ: imagej.net/plu...
StarDist: imagej.net/plu...
Alternative method (better results, a bit more complicated) : "Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle" (www.nature.com...)
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[Author]
Yannick Krempp, bioimage analyst at the CIF platform, Lausanne, Switzerland
cif.unil.ch​​
/ yannick-krempp

[@user-kr1ee1dv1c]

Thank you very much for sharing such an informative tutorial. One of the best thing for me was the easy step-wise explanation for every sub-step. Keep on this good work

[@fujueji438]

so good.

[@AlessioTorcinaro]

Is it possible to segment nuclei with StarDist, after using a Colour Deconvolution for H/E or from original H/E image?
I have triend with both images as input but it looks like StarDist segment preferably larger objects (i.e. muscle fibers) rather than nuclei. The result is that lots of nuclei are included in the muscle fiber object.
This happens to me also if I use Hematoxylin image, obtained after using Colour Deconvolution.
Thank you for your tutorial!

[@UniversalBuilder]

Stardist is quite sensitive to the cell (or round object) size.
A good trick is to scale the image according to what stardist expects. Example of the issue here with a blob image scaled up 8x : ibb.co/hLh9Hch .
Another way to improve woud be perhaps to try to do a better deconvolution. If not possible, try to resort to pixel classifiers plugins like weka or labkit to create a mask of the regions you want to exclude, then use stardist on the masked copy of the image

[@AlessioTorcinaro]

@@UniversalBuilder Thank you for the suggestions! 😎

[@davidcastro7797]

Hey Yannick, many thanks for your help and efforts!! Could you please kindly tell me what kind of staining technique you're using in the sample image?

[@UniversalBuilder]

This wasn't my sample so I'm not 100% sure but this was probably a simple hematoxylin and eosin labeling.

[@annakocharyan8447]

Thank you very much for the tutorial. Do you have any advice for automated quantification for IHC-stained muscle fibers? I have to count the positive fibers(not cells) and have been doing it manually. Thank you in advance!

[@UniversalBuilder]

I think you could probably do a variation of the tutorial above and add another step to discriminate between the fibers, but it's difficult to say without knowing what you consider as "positive". Is it a change of size or shape ? Of staining color ? Of texture ? Depending on that you could use tools to get information from the ROIs created using this tutorial to create a new heat map based on the measurements, and threshold it to keep only the fibers you estimate as positive. The Biovoxxel and MorpholibJ plugins have tools for that if I recall correctly.

[@annakocharyan8447]

@@UniversalBuilder , thank you very much for your response!

[@guozhengzhu532]

Great video! I would like to know How do you determine the parameters of Stardist?

[@UniversalBuilder]

Thanks ! You can find some answers here : imagej.net/plugins/stardist
And there's a FAQ (that also applies to training models and the python version) here stardist.net/docs/faq.html
In general, start with the suggested parameters for the model. If it doesn't work well, try first to check the size of your images to see if they need to be resized to get a proper segmentation.
The rest depends on your images, so you might have to check one parameter at a time and see how changing it improves or not the results.

[@richashrivastava3981]

Can you please tell what magnification image you have selected here?

[@UniversalBuilder]

If you are referring to the acquisition with the slide scanner it was probably the 10x plan apo

[@parislloyed5413]

Hello, Thankyou for the video. Wanting to confirm if the label part at the end is the number of muscle fibres?

[@UniversalBuilder]

Slightly late to answer (sorry didn't see this one), Yes it's the number of particles analyzed.

[@rubalsingh7885]

Hi
Thanks to this video tutorial. Can we do this Segmentation in 3D cultured cells and if yes please make one video on 3D cultured cells Segmentation.

[@UniversalBuilder]

Hi, it's possible but I wouldn't suggest using Fiji for that. I would use something like Imaris, or maybe try other tools like APEER, ...
In order to enable 3D segmentation with StarDist and Fiji you have first to install and configure a proper Python environment and the StarDist full package.
Then you can use a wrapper to connect from Fiji to your Python installation. There are instructions here : github.com/BIOP/ijl-utilities-wrappers
The rest of the workflow should be quite similar.
As of making a video for that, I'll see if I have time but no promises there, as I'm really too busy in the moment :(

[@rubalsingh7885]

@@UniversalBuilder Thanks for your suggestion I will try to do