Comments (5)
Hi,
you will have to be a little bit more specific if you want me to help you ;)
About the documentation: pretty much all the info is either in the code/tutorials or in the paper. You can also have a look at the following issues, where I had a fair bit of re-explaining to do because someone was trying to make the tutorial 7 work but was not using the right inputs.
#45 ValueError: axes don't match array" in sample_segmentation_pairs_d.py
#46 ValueError: axes don't match array in prediction of S1 unit at 7-synthseg+.py
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Thanks for the quick reply. I'm getting when I attempt to create an instance of the RandomSpatialDeformation layer, please see the screenshot attached
my inputs to the sample_segmentation_pairs_d function are:
image_dir = './path_to_images_folder'
labels_dir = ['./path_to_labels_folder']
results_dir = './path_to_results_folder'
n_examples = 5
path_model = "./dice_100.h5" (trained s1 model)
segmentation_labels = "segmentation_labels.npy"
n_neutral_labels = None
please let me know if I can provide more info! Thanks
from synthseg.
Sorry for the very late answer...
You have an old version of the code, try pulling the latest version :)
from synthseg.
Thank you for the response, I did manage to get there myself!
I have another issue with the outputs of the sample_segmentation_pairs_d function if you could please provide some more assistance.
I am using the same parameters as outlined above, but I'll repost here:
image_dir = './path_to_image.nii.gz'
labels_dir = './path_to_corresponding_label.nii.gz'
results_dir = './path_to_results_folder'
n_examples = 5
path_model = "./dice_100.h5" (trained s1 model)
segmentation_labels = "./segmentation_labels_s1.npy" (used to train s1 model)
n_neutral_labels = None
When used to generate results, I get some paired outputs that look appropriate to train the denoiser, and other outputs that are pure noise (see below). I'm thinking that there is some parameter used to build the augmentation model that, when randomly sampled outside of a particular range, is causing the noisy output. However I'm not sure what this may be and I've tried playing around with the upper/lower bounds of the augmentation parameters without any luck so far to try and produce consistently appropriate outputs.
Would you have any potential view on what may be causing the noisy output? Any help would be great thanks!
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again, sorry for the late answer.
I also got some pretty messed up segmentations with the default parameters, but never like that. The worst ones are often almost empty, but I never observed those patterns. But then I guess it depends on the trained and fixed network that you used to obtain those segmentations. Different trainings lead to different networks that respond diffrently to extremely augmented images.
But then is it really a bad thing ? the denoiser might need to see some of those very bad examples during training, because even if they're hopeless to "denoise" (ie reconstruct), that could still help the denoiser learning what's a good or bad segmentations. So I suggest keeping those guys in for now, and see how removing them affects the training of the denoiser (accuracy, stability, etc.).
Let me know how it goes
Best
Benjamin
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Related Issues (20)
- Input shapes at inference HOT 7
- /
- provide bids-app HOT 1
- Run inference directly on the original NIFTI image HOT 2
- CSF label left out of "labels table.txt" HOT 1
- Synthesize image for different anantomical templates HOT 2
- Resampled image is not saved HOT 2
- random segmentation failures HOT 3
- Segmentation Discrepancies Analysis HOT 3
- Clarification on Fine-Tuning with real images HOT 4
- \
- ValueError: Error when checking input: expected labels_input to have 4 dimensions, but got array with shape (1, 216, 256, 8, 1) HOT 1
- Clarification on GMM sampling HOT 1
- InvalidArgumentError: 4D Input Expected with SynthSeg_predict.py on MacOS M2 HOT 7
- insula subsegmentation HOT 2
- Error while following the training example HOT 6
- synthseg-robust 2.0 HOT 1
- lab2im version errors HOT 2
- Force resampling HOT 1
- Tutorial for re-training SynthSeg on non-brain datasets HOT 1
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