marcniethammer
marcniethammer
I am not sure what you mean by 3D images with two different dimensions, but the method itself is currently geared at working with 3D patches. See: https://arxiv.org/abs/1703.10908
This is not what is it designed for. However, since it works based on patches there may be some hope that it could work. This would, of course, requires some...
Looks like this is a multi-modal test case. Can you run a case without instance optimization? If this works then it is possible that switching the similarity measure from 1-LNCC...
Also, uniGradICON is trained for 3D whereas I assume your WSI are in 2D. GradICON can be trained for 2D as well (but we don't have a general model for...
This model is currently focused on 3D. For 2D a starting point could be #17 But ultimately it would be useful to train a dedicated 2D model.
Are you talking about inference or training? For inference, there are no parameters other than the number of instance optimization (IO) steps and you can pick the similarity measure most...