Can Qupath be used to quantify non-cellular staining/features?

[ghu_gha]

I have stained for collagen in my tissues with Sirius Red stain, I have attached one of the images below. Qupath seems to be adapted to quantify histological images where cells are also stained.

Is it possible to quantify image stianing when cells are not stained but tissue components such as collagen?

[Research_Associate (MicroscopyRA)]

If by quantification you mean area, that is fairly straightforward using either the pixel classifier or simple thresholding tool in M10/M11, or the Positive Pixel detection in 0.1.2. I have never seen anyone trying to differentiate between the two areas you have indicated, and from the quality of image provided can’t see differences.

It may be that a pixel classifier could pick them up if there are texture differences, but I don’t think a simple thresholder would suffice to separate the two types of tissue.

@smcardle and @Zbigniew_Mikulski have more experience with PSR and might have other suggestions.

[ghu_gha]

Thanks a lot for your helpful response. I want to see the area of the image occupied by the fibrotic areas which I have enclosed in a selection with red borders. Fibrotic areas as well as connective tissues near airways have higher collagen content but the latter is normal. I am not able to set a colour threshold to separate the fibrotic and normal areas.

My objective is to define areas of fibrosis, have appttern and apply to all other images I have from the same staining experiment.

[Research_Associate (MicroscopyRA)]

@ghu_gha

Fibrotic areas as well as connective tissues near airways have higher collagen content but the latter is normal. I am not able to set a colour threshold to separate the fibrotic and normal areas

Yes, with that problem, you would need to try the pixel classifier, and make sure you try out many of the alternative “Features” possible, and run it at very high resolution. It will be… slow.

You might also try Weka pixel classifier through FIJI, or Ilastik.

[ghu_gha]

Thanks a lot.So I can run the pixel classifier on the fibrotic areas and then apply the criteria to all my other slides?

[Research_Associate (MicroscopyRA)]

That should currently be possible through scripting.

QuPath v0.2.0-m5 now available

Actually, after doing some spelunking into the source code I came up with this script which seems to get the job done! import qupath.lib.gui.ml.PixelClassifierTools //Need a full image annotation to begin with createSelectAllObject(true) def annotations = getAnnotationObjects() //Define pixel classifier def project = getProject() def classifier = project.getPixelClassifiers().get('2019-11-04 Pixel Model') //Define image data def imageData = getCurrentImageData() //Convert pixel classifier t…

[smcardle (Sara McArdle)]

I’m going to jump in here and recommend superpixels. I love them for acellular regions like this. I find that they are better at “seeing” large scale features than the pixel classifier. @Research_Associate has a great description of them here.

The general workflow would be to 1) generate superpixels, 2) calculate features on those regions, including basic intensity features and haralick measurements and anything else you think might be helpful, 3) train an object classifier to identify fibrotic vs non-fibrotic regions, 4) classify all the objects in all images in your project, 5) (optional) use “tile classifications to annotations” to merge the detection objects into annotation objects (with a recent bug fix in m10), 6) measure the area of the classified objects.

[ghu_gha]

Thank you so much. In step 5 of the workflow you have kindly proposed, I need to classify all objects in all images in my project. Can this be done automatically or I have to door manually?

[Research_Associate (MicroscopyRA)]

This might help as well.

QuPath Guide: Creating classified annotation areas using SLICs

Quick tutorial on generating annotation level areas from SLICs. Image courtesy of La Jolla Institute of Immunology Microscopy Core shared image resource. Not going to go into a whole lot of details here as the entire method will likely be obsolete once the Pixel Classifier is fully functional. Generate your primary area with an annotation, either simple tissue detection, createSelectAllObject() or something else like drawing a square. Generate your SLICs. There isn’t any one right answer fo…

I’m not sure if this was the bug fix @smcardle was mentioning, but there may be some issues with the old detection classifier in M11.

Saving/loading training objects for cell classifier in QuPath M11

This morning I wrote a little bit about what ‘deprecated’ means in this context at https://qupath.readthedocs.io/en/latest/docs/reference/faqs.html#why-are-some-commands-marked-as-deprecated I also created an enhancement request regarding training the classifiers from multiple images here. I completely agree this is necessary; the fact it doesn’t yet exist is a big part of the reason why the ‘old’ classifier approach remains for now. They have only deprecated and not removed because I understa…

[smcardle (Sara McArdle)]

After you have trained and saved a classifier, you can write a script to apply it. In m11, using the new object classifier, the command is simply:

runObjectClassifier(“NameOfClassifier”);

Once you have a script to do the whole workflow (generating super pixels through classification), you can use “Run for Project” to do all the images.

Note: I highly recommend training your classifier on a combined training image (Classify > Training Images > Create Combined Training Image) for better accuracy across a project.

[ghu_gha]

Thank you very mych. But when I do Classify > Training Images > Create Combined Training Image I get the error message : “No suitable annotations found in the current project”.

[Research_Associate (MicroscopyRA)]

Create combined training image requires annotations of the class chosen to exist throughout the project. Have you created annotations with the same assigned class in several of your images?

One further thing to note when creating those… it will only collect saved annotations. Drawing them is not sufficient. I have occasionally omitted the last image in a set when using that feature as I forget to save the current data set before Creating the combined training image.

[ghu_gha]

Please which steps do I need to “train an object classifier to identify fibrotic vs non-fibrotic regions”?

This is how I tried to do it:

• to set annotations on one loaded images:

  • polygon > annotations > right click > set properties > name = “fibrotic”

  • polygon > annotations > right click > set properties > name = “non-fibrotic”

  I defined on the same slide image 5 fibrotic and 5 non-fibrotic zones

• To train an object classifier to identify fibrotic vs non-fibrotic regions

  • Classify > Object classification > Train object classifier > save & apply

  • Then I get the error: java.lang.NullPointerException

[Research_Associate (MicroscopyRA)]

You may want to look into the classification steps mentioned in general to understand how QuPath works. You simply changed the name of the annotations, not their class. The name is only there for your benefit, the program doesn’t really make use of it.
https://qupath.readthedocs.io/en/latest/docs/tutorials/cell_classification.html#annotate-regions-containing-different-cell-types

[smcardle (Sara McArdle)]

There’s also step-by-step instructions for building an object classifier on Youtube.

[ghu_gha]

Thank you so much for your kind help. I wish to ask for a little clarification. So if I got you well, is this how the work flow from scratch will be:?

1. Create project

2. Add images

3. build an object classifier

4. generate superpixels like this:
   Analyze->Region identification->Tiles &
   superpixels->SLIC superpixel
   segmentation.

5. calculate features on those regions, including basic intensity features and haralick measurements and anything else you think might be helpful,

6. train an object classifier to identify fibrotic vs non-fibrotic regions,

7. classify all the objects in all images in your project,

8. (optional) use “tile classifications to annotations” to merge the detection objects into annotation objects (with a recent bug fix in m10),

9. measure the area of the classified objects.

Thank you in advance

[Research_Associate (MicroscopyRA)]

3. isn’t correct as you wouldn’t have any objects to build the classifier with at that point. Also it is redundant with 6.