Comments (1)
Hi,
the original problem was the following: If you resample the HCP image from 1.25mm to 2.5mm resolution you get [73,87,73]
. When you upsample this back to 1.25mm you get [146,174,146]
. So unfortunately this is different from what you had in the beginning. Now you would have to remember if you downsampled from a uneven shape and then cut one value if you upsample again. That's what is done in TractSeg
. For ExpRunner
I did not bother doing this. If I need the output to be [145,174,145]
I just do data = data[:-1, :, :-1, :]
, or use TractSeg
for inference (using the branch speed_up
you can do TractSeg ... --exp_name <your experiment_name>
and use TractSeg
for inference with your custom experiments instead of doing ExpRunner --seg/--probs
.
The code for training your own models (ExpRunner
) is not as polished as the code for using the pretrained models (TractSeg
) so unfortunately it is a bit more inconvenient to use.
I hope that answered your question.
from tractseg.
Related Issues (20)
- Import Error HOT 6
- Dice Score
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- RuntimeError: unexpected EOF, expected 31322553 more bytes. The file might be corrupted HOT 2
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- Downloading pretrained weights (~140MB) seven times for every subject...can we have a better solution
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from tractseg.