Dear Christopher,

I am aware that output of W2MHS is a probability map. What I tried to point out was that when we run W2MHS on our acquisitions we don't obtain satisfactory results.
To clarify this I attached some screenshots that attempt to highlight the problem (I uploaded them in a .zip file as I cannot upload more than one file separately).
In the first image (cuttingP06) you'll find the registered FLAIR on the background with the output p-map in the foreground in hot colors, for this case the threshold in the viewer was set to 0.6 meaning that all the voxels (in the p-map) which are lower than 0.6 are discarded. Clearly, the problem here is that there are huge areas in where the probability is quite high (bigger than 0.6, and therefore will be taken into account in the quantification step) but there are NO HYPERINTENSITIES.
In order to solve this problem we set the threshold higher, lets say 0.7 (you will find the second image named cuttingP07). Now this big areas have dissapeared (meaning that its probabilities where below 0.7), and it looks better. But when we look a little closer, we see that the areas where we can really SEE HYPERINTENSITIES in the FLAIR image, have become way bigger than the actual output in the p-map over that area. To illustrate this, you can find the third image (cuttingP07localEffect) in where this area is highlighted in green.
This results are not satisfying and differ a lot from what I have found on the paper "Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer's disease risk and aging studies.".

I would guess that the problem lies in the training step, and because our acquisitions might be a little different than the ones you used for training (e.g. different scanner although same protocol) then the results at testing stage are not satisfying. If you agree, then could you please precise the way you trained your model and provide the code necessary for it?

Thanks a lot for your time.
Best regards,
Nicolas VINUESA