Classifier with stromal cell intensities

Image Analysis
#1

Hello! I am trying to set up a classifier that detects tumour and stroma and classifies the cells as one of the two. I have been able to utilize the intensity features to set up the tumour cell intensities, but I was wondering how I would be able to do so for the stromal cells too? (I’m using v0.1.3 and can’t update to a new version as I’ve already run multiple analyses and want to keep it consistent.)

Is this possible? If so, how would I go about implementing it? If not, what might be another option for me (ex. scripting?)?

Thank you so much for all of your help!

#2

Sample images would be helpful, along with some indication of what is or is not working. Otherwise we are working blind :slight_smile:

#3

ZEB1
ZEB11641×1057 2.92 MB

Oh, sorry! I’ve attached a picture. I created a detection classifier and ran it using tumour and stromal parameters. I added the thresholds under the intensity features of the classifier based on the Nucleus OD mean. It ran on the tumour cells (blue, yellow, orange, and red).

I want to also be able to classify the stromal cells (in green) with intensity features to be able to differentiate between light, intermediate, and high stain intensity. However, I am not sure how to go about this. Let me know if there’s anything else you’d like me to provide!

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#4

Ah, perfect. That was discussed here:
https://forum.image.sc/t/setcellintensityclassifications-for-a-certain-class/33347/3?u=research_associate
Another way to write the script would be:

def measurementName = "Nucleus: Hematoxylin OD min";
def thresholds = [0.05, 0.2] as double[];

pathObjects = getCellObjects().findAll{it.getPathClass() == getPathClass("Stroma")}

setIntensityClassifications(pathObjects, measurementName, thresholds);

And if you are having trouble with stromal cytoplasmic staining classification, it might be worth taking a look at:

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#5

Oh, thank you!

I tried the script and it works (thank you again!) but is it possible to modify the script to add stroma: num 3+?

I’ve attached a picture of the measurements that I get.
image

#6

There were only 2 thresholds for stroma, so there are only 1+ and 2+ stromal cells. You can adjust the script to do that if you want, and you should be adjusting the thresholds anyway!

#7

Thank you! I already adjusted the thresholds in the script that you wrote up.

I also did add another threshold to the list, but it didn’t reflect in the measurements. When I run it, I am still only getting stroma: num 1+ and stroma: num 2+.

#8

Not sure without seeing the code. The other possibility is that it worked, but you just are not seeing it. Can try adding an update line to the end, but I was able to see 1 2 and 3+ in my image.

def measurementName = "Nucleus: DAPI mean";
def thresholds = [2000, 3000, 4000] as double[];

pathObjects = getCellObjects().findAll{it.getPathClass() == getPathClass("Tumor")}

setIntensityClassifications(pathObjects, measurementName, thresholds);
fireHierarchyUpdate()
#9

*And by “just not seeing it,” I want to be clear that means the results are not updating in the interface :slight_smile: But if you check the classifications of the cells in the Detections output or otherwise, they should be accurate.

#10

Thank you! I tried the script but it still doesn’t show me the num 3+ in the interface or in the output.

The script below is the one I used that worked but only provided stroma: num 1+ and num 2+.

def measurementName = "Nucleus: DAB OD mean";
def thresholds = [0.05, 0.27, 0.42] as double[];

pathObjects = getCellObjects().findAll{it.getPathClass() == getPathClass("Stroma")}

setIntensityClassifications(pathObjects, measurementName, thresholds);

Thank you again!

#11

I’m not sure at this point. I copied your script in and added the update line, and it gave me 3+ cells. It should work in 0.1.3 (and 1.2, and 0.2.0M8, really)

image
image1098×514 353 KB

I assume you have some cells you know should be above your 3+ threshold. What are they being classified as when you select them?

#12

Oh, okay. The cells above the 3+ threshold are being classified as 2+.

#13

If the cells are already classified as 2+ then the script as it is written above won’t reclassify them.

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#14

Oh? For me it has been. I can swap any of the numbers around, re run it, and all of the cells update. Again, with the update line included.

Hmm, yep, Pete is right. It works if I don’t pick out a base class! I wasn’t working with tumor/stroma.

#15

Which I guess means you only get one shot at it, or you need to reset the thresholds by setting any class that is stroma back to stroma.


pathObjects = getCellObjects().findAll{it.getPathClass().toString().contains("Stroma")}
pathObjects.each{it.setPathClass(getPathClass("Stroma"))}

Try that first, then run the script with 3 levels.

Alternatively, the above script could be modified to include that line:

def measurementName = "Nucleus: DAB OD mean";
def thresholds = [0.05, 0.27, 0.42] as double[];

pathObjects = getCellObjects().findAll{it.getPathClass().toString().contains("Stroma")}

setIntensityClassifications(pathObjects, measurementName, thresholds);
#16

The easiest solution is if you apply the same intensity classifications to all ‘base’ classes, then just use

setCellIntensityClassifications('Nucleus: DAB OD mean', 0.2, 0.4, 0.6)

or similar, as described here.

Otherwise I suspect you’ll need to replace it.getPathClass() with it.getBaseClass().getPathClass() (although I don’t recall if that existing already in v0.1.3 or not… if not then @Research_Associate’s String trick should also work, unless you have ‘Stroma’ turning up in other classification names somehow).

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#17

Yeah, I missed that no cells were being picked up after the first run due to the pathObjects line. My fault.

getBaseClass does not seem to exist in 1.3

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#18

It works! Thank you so much for all of your help!

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