Open tischi opened 4 years ago
The method to detect infected cells is really simplistic at the moment: Subtract the mean background value of the marker channel, then compute 50th brightest pixel in that channel per segment. If that intensity is greater than 250, the cell is declared as infected. This can obviously fail if
We could adress 4. by denoising the image, e.g. simply by gaussian smoothing. But I think we just really need to come up with a better method that is also robust to the other points.
There are also cases of weakly infected cells (the one in the middle of below screenshot) Interestingly the serum response is quite strong in this cell:
Here is a region which is quite noisy and indeed there are many false positives:
@imagirom Where does the threshold of 250 come from?
Grid search and evaluation on the plates vibor has provided ground truth for (excluding the titration plate). So it might very well be overfitting to these plates.
Ok, let's say the fluorescence lamp that Vibor uses on his microscope gets a bit dirty and everything is a bit dimmer, would that still be the right value? I mean is the 250 normalised to anything you just an absolute gray value?
It is an absolute gray value. As I mentioned before, I totally agree that we need something better here.
Ok! I started an issue about it: https://github.com/hci-unihd/antibodies-analysis-issues/issues/17
{"plateName":"titration_plate_20200403_154849","siteName":"F08-0003","pixelLocation":[22132.557453701334,15205.579169115894,0.0]}
Another example of a wrongly (infected) classified cell. I do not really get why, because the cell segmentation looks good, i.e. no overlap with the surrounding infected cells.