Training Image Creation
The Positive Cell detection in QuPath is not accurate enough for very acellular tissue as the cell detection always recognises some tissue artefacts as cells, therefore we train an image.
To create a training image on which we can train QuPath to recognise positive cells, negative cells, and no cells we need to choose our regions of interest.
If you want to run the analysis on different tissue components, a training image for each of them needs to be created.
The same applies if multiple sections are stained with different antibodies on one slide.
If you have different regions and antibodies, create a separate project for each antibody.
Click on ›
› 
(on the three dots) › Add/Remove › Add classFor my project, I have chosen the following classes,
Region
Ignore*
PositiveCell
NegativeCell
NoCell
either
TissueComponent1 (e.g. CEP)
TissueComponent2 (e.g NP)
TissueComponent3 (e.g. iAF)
TisueComponent4 (e.g. oAF)
or
Antibody 1 (e.g. IL1)
Antibody 2 (e.g. IL6)
Colours can be changed by double-clicking on the coloured square in front of the class.
The classification of the PositiveCell, NegativeCell, and NoCell must be set as such with exactly this spelling as those are used for the calculation of the immunopositivity rate with the Python script.
The other classes can be freely chosen. Check again that you have no spelling mistakes because this would lead to an error in the final code we are running.
Once the classes are added choose e.g TissueComponent1 and click on auto set, so that it automatically sets whatever you draw to the according class.
By clicking on
a raster will appear which can help draw squares of the same size (3x3). Fifteen to twenty regions in different slides should be chosen and saved as changes. They must represent the whole project and contain all sorts of different regions containing cells (pos/neg), tissue folds, dirt on the slide, etc. The more regions chosen to train on, the better the outcome will be, however too many can slow down the computer. Furthermore, the classifier will be optimised on additional regions later on.
Repeat this step for every TissueComponet analysis.
Once all the different regions are chosen a training image is created by choosing under classify, training images, create training image. Change the classification to TissueComponent1 and click on Ok.
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