Normalization of CT scans I trained an infection segmentation models on a large dataset of CT scans, and want to extend it to other datasets to show the ability of the model to generalize. What I found though, is that CT scans look very different. For example,

CT scan from the first datasets,

Image from another dataset


I tried three things:

Global mean/standard deviation (subtract from each image the same vector $m$ and divide by $s$),
Local mean/standard deviation (from each image subtract its $m$ and divide by its $s$, os that every image is normally distributed with mean $m$ and std $s$,
Gamma correction ($\gamma=0.01$), see the image at the bottom.

None of which worked. My question is: is it possible to 'normalize' other CT scans, 'normalize' in a sense make them more similar to the one on which I trained the model?
There is an extensive literate and established software to normalize CT scans. It is required for medical research to conduct group level analysis. The most common process involves transforming to standard template. The paper &quot;Age-specific CT and MRI templates for spatial normalization &quot; goes into greater detail.