Ah, if you need to apply the measurements as a summary to the TMA, that would take a little more work - but with your diagram it looks pretty easy.
It looks to me like you are using a simple application of the multiplex classifier I linked to above. You have two object classifiers one for tumor/immune, and one for foxp3. Also, I do not recommend using the same class multiple times like this - it is confusing both for the people you are talking to, and likely for downstream analysis. Only use any one class once. So in this case, either label the tumor annotation as a different class, or the tumor cells.
You still will not be applying the measurements to the TMA, you can’t do that by default and it would require scripting. Outputting the cell data for processing will still get you
Parent field: indicates TMA (stroma) or Tumor tissue - you can use this to stratify your data
You will probably need to rename your cells based on the TMA they are inside of, or rename your annotations based on the TMA they are inside of.
Class: some combination of Tumor/Stroma and FOX+/FOX- - Once you have two individual classifiers, the Composite classifier will generate a two part class.
So collecting your data will still require some additional coding, but that’s the basic workflow.
Alternatively - if you literally just want the immune cells and do not care about the tumor cells, why not use your Tumor pixel classifier to classify cells, then Set Cell Intensity Classifications based on the FoxP3? You would end up with tumor or unclassified cells (based on the pixel classifier) and then Positive or Negative based on the FoxP3 threshold you choose. Give that a shot first if it gets you the data you need.
No need for annotations, and the cell counts apply directly to the TMA measurement list.