Classification results for a whole population

Hi Guys,

My experiment is as follows:
<5000 images
Stained for DAPI, Actin and a tracker probe on a subpopulation of cells.

I have a single dataset, but would like to classify it twice and compare the two results. I have one set of rules which use the tracker stain to detect the sub population, and this has an accuracy of ~98% - perfect! I then remove that data by excluding those columns in my properties file, and reclassify with the same training set using the other fluorescent channel, which is a simple actin stain, with measure areashape, intensity, texture etc in CP. This is currently about 70% accurate and should improve with some better microscopy.

Now, I can score all images, and compare the total number of positive and negative cells with both classifier runs, and this tells me that there is a 1.5% reduction in positive cells in the whole data set. This does not take into account cells changing from positive to negative and vice versa though.

Is it possible to get an output of each individual cell and it’s classification, so I can calculate the real error between the two rule sets? I’ve tried adding dynamic groups with imagenumber and objectnumber but this doesn’t give me a table with unique cell classification data.

The only way I can think of doing this is exporting my rules and re-running cell profiler with two rule based filters on the end of the pipeline (one including the tracker and one excluding), but this would take 8 hours. Then I could use the filter results columns to compare the two rule sets

Any suggestions would be much appreciated

Also, is it possible to extract the data for the Cross-Validation Accuracy graph so I can re-plot it?

Whoops! The answer to my question was on the forum already

That thread mentions that sometimes CPA won’t automatically create the Per_Class table that I want - I’m finding this as well (r11710) but it’s intermittent and I can manually create the table in CPA and it works perfectly.