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?