Iskender Kahramanoglu

Thanks for your reply. I will try soon, and give you feedback.

> > Thanks for this special project. > > I trained my own dataset, finished all stage. But i want to give a specific source and target image, and generate...

> I believe the problem here is with your labels, not a low resolution model. > > For a simple test, try enlarging your labels and see if it works,...

Hi @FabianIsensee , do you have any idea about this?

Today I tried to take inference a nifti with 1000x1000x1000 size and (0.2, 0.2, 0.2) pixel spacing. 32 class model takes inference in 16 seconds, but save as nifti spents...

Hi @FabianIsensee , do you have any idea about this?

> You can try to split the bigger volume into multiple smaller patches. For example, you can split it into 9, 16 or 25 patches. Then you do the inference...

> Overlap between patches is not difficult, nnUNet already does this. You just need to patchify with overlap once more in order to reduce RAM usage and to speed up...

OK, I will try to split a file. Thank you very much.

> I suggest to physically split the test nifti file (you can use the patchly library). You can't change the "patch_size" parameter in the json, unless you want to retrain...