I have a case where there are multiple ROIs representing multiple lesions from a brain metastasis patient. Running a voxel-based extraction on two of these ROIs independently provides different answers than when I extract features on a combined ROI of the two lesions, then separate it after. This is very disturbing.
This tells me that the voxel-based extraction is not localized, and will always depend on what’s going on in other voxels. I’d like to train a classifier based on the values from individual voxels, but the pipeline used to extract these voxels i.e. combined ROI or individual, makes the classifier invalid.
I’m currently attempting to simply extract features across the entire brain, from which I’ll collect from the appropriate ROIs later… but it seems like this is going to be prohibitively slow.
How can I extract feature maps without the values in one voxel being determined by distant values? Is the voxel-based extraction local? what does the kernel size mean if this test-case fails?