there's this one
and the other one is empty
Hi! I am trying to register a light sheet scan of a whole mouse brain using amap with the default parameters. The registration performs reasonably well, but there seems to be consistent mismatch between the borders of the tissue and the outside edges of the atlas. Any tips on how to more accurately align the edges? Our protocol has steps to reduce tissue autofluorescence so the signal:noise is very poor - background is around ~200 while signal ranges between 250-450 with a 16-bit histogram
12 replies
If you open the downsampled image, then overlay the affine registered image, you should see an image that’s roughly aligned with your data.
The free form registered image will then be the properly (or not in your case) aligned image.
If the affine image is of the right size, and roughly aligned with your brain, then the problem probably lies in the free form step.
I’d like get this fixed though, so I’m happy to have a quick chat on zoom, or if you can send me some data I’ll take a look.
The affine_registered_atlas_brain.nii (magenta) overlays fairly well over the downsampled image (green).
I'll send you a link to the raw data privately. Thanks for your help!
Hi !
I have a problem regarding the training of the network.
When I use the curation command, this line appears just after my command :
WARNING: Cannot mix incompatible Qt library (version 0x50c03) with this library (version 0x50c09)
Do you have any idea how to fix it ?
Thanks for your help !
I have a problem regarding the training of the network.
When I use the curation command, this line appears just after my command :
WARNING: Cannot mix incompatible Qt library (version 0x50c03) with this library (version 0x50c09)
Do you have any idea how to fix it ?
Thanks for your help !
6 replies
Hi @janfpl, I had a quick look at your data, and it seems like some of the registration problems might be due to the data itself (damage to the front of the brain, what looks like stitching artefacts, and some bright signal outside of the brain). I'll have a play around to see if I can improve it, but interestingly, using the 25um resolution atlas in brainreg (https://github.com/brainglobe/brainreg) seems to look a bit better than the image you sent (see above).
It's still not great though.
If you want to try out brainreg, this is the command I used:
brainreg /path/to/data /path/to/output -x 3.6498 -y 3.6498 -z 15.3767 --orientation lsp --atlas allen_mouse_25um
Hi!
I am having a problem with registering a brain for a current project. Right before the Debug process starts to run I receive this error:
Traceback (most recent call last):
File "c:\users\administrator\miniconda3\envs\cellfinder\lib\concurrent\futures\process.py", line 239, in _process_worker
r = call_item.fn(call_item.args, *call_item.kwargs)
File "c:\users\administrator\miniconda3\envs\cellfinder\lib\site-packages\brainio\brainio.py", line 465, in load_from_paths_sequence
return volume
UnboundLocalError: local variable 'volume' referenced before assignment
This error only occurs with this one brain. All other brains I register succeed. Thank you for your help!
If it still doesnt work, if you can share the data with me, ill take a look.
HI Adam. I just have a question regarding the generation of the training data. there are a lot of cells that are considerate non-cells by the classification, so I think I have to retrain the network properly. But do I have to generate these training data for the whole brain to generate the Yaml files ? Or I only can do the correction for some plane ? Thanks for your help
Hi @Chouchi974, I've answered your question over at https://gitter.im/cellfinder/celldetection, so that others can find the answer in the right place.